AI Research Engine — self-hosted knowledge acquisition system

- 10 MCP tools via thin proxy on MacBook - Backend REST API + Dashboard on CT 145 Docker - Services: YaCy crawler, OpenSearch index, Qdrant vectors, Ollama LLM - All 4 services healthy and verified
2026-08-04 06:02:42 -07:00
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+.venv/
+__pycache__/
+*.pyc
+.env
+.git/
--- a/11
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+FROM python:3.11-slim
+
+WORKDIR /app
+
+RUN pip install --no-cache-dir fastapi uvicorn httpx python-dotenv
+
+COPY backend.py .
+
+EXPOSE 8000
+
+CMD ["python", "backend.py"]
--- a/README.md
+++ b/README.md
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+# 🧠 AI Research Engine
+
+**Self-hosted agentic AI search infrastructure** — your private research cloud.
+
+AI agents connect via MCP to discover, crawl, index, and synthesize knowledge from the web — all running on your own Proxmox hardware.
+
+## Architecture
+
+```
+┌──────────────────────────────────────────────────┐
+│  MacBook (thin MCP proxy)                        │
+│  server.py → forwards to CT 145 backend          │
+└──────────────┬───────────────────────────────────┘
+               │ HTTP
+┌──────────────▼───────────────────────────────────┐
+│  CT 145 (10.30.20.249) — Docker Host             │
+│  ┌─────────┐ ┌──────────┐ ┌───────┐ ┌─────────┐ │
+│  │  YaCy   │ │OpenSearch│ │ Redis │ │ Backend │ │
+│  │  :8090  │ │  :9200   │ │ :6379 │ │  :8000  │ │
+│  │ crawl   │ │  index   │ │ cache │ │  API+UI │ │
+│  └─────────┘ └──────────┘ └───────┘ └─────────┘ │
+└──────────────────────────────────────────────────┘
+               │                    │
+┌──────────────▼─────┐  ┌──────────▼──────────────┐
+│  CT 509 (.68)      │  │  GamingPC (.186)        │
+│  Qdrant :6333      │  │  Ollama :11434          │
+│  semantic search   │  │  ornith:latest (9B)     │
+└────────────────────┘  └─────────────────────────┘
+```
+
+## Endpoints
+
+| Service | URL | Purpose |
+|---------|-----|---------|
+| Dashboard | http://10.30.20.249:8000 | Web UI |
+| Backend API | http://10.30.20.249:8000/api/* | REST API |
+| OpenSearch | http://10.30.20.249:9200 | Full-text index |
+| YaCy | http://10.30.20.249:8090 | Web crawler |
+| Qdrant | http://10.30.20.68:6333 | Vector DB |
+| Ollama | http://10.30.20.186:11434 | LLM inference |
+
+## MCP Tools (10)
+
+| Tool | Description |
+|------|-------------|
+| `search_web(query)` | Full-text search across indexed documents |
+| `semantic_search(query)` | Vector search by meaning (Qdrant) |
+| `crawl_url(url)` | Crawl a URL into the index |
+| `crawl_topic(topic)` | Discover + crawl sources for a topic |
+| `research_topic(topic)` | Full pipeline: search → crawl → summarize |
+| `retrieve_document(url)` | Get full indexed document content |
+| `summarize_sources(urls)` | AI summary of multiple sources |
+| `extract_information(url, schema)` | Structured data extraction |
+| `create_report(topic)` | Comprehensive research report |
+| `index_status()` | System health check |
+
+## Quick Start
+
+```bash
+# Check status
+curl http://10.30.20.249:8000/api/status
+
+# Search
+curl "http://10.30.20.249:8000/api/search?q=knowledge+graphs"
+
+# Crawl a URL
+curl "http://10.30.20.249:8000/api/crawl?url=https://example.com&depth=1"
+
+# Deep research
+curl "http://10.30.20.249:8000/api/research?topic=LED+grow+lights"
+
+# Generate report
+curl "http://10.30.20.249:8000/api/report?topic=AI+agents"
+```
+
+## Deployment
+
+On CT 145 (10.30.20.249):
+
+```bash
+# Services
+docker run -d --name redis --restart unless-stopped -p 6379:6379 redis:7-alpine
+docker run -d --name yacy --restart unless-stopped -p 8090:8090 yacy/yacy_search_server:latest
+docker run -d --name opensearch --restart unless-stopped -p 9200:9200 \
+  -e "discovery.type=single-node" -e "DISABLE_SECURITY_PLUGIN=true" \
+  -e "OPENSEARCH_JAVA_OPTS=-Xms1g -Xmx2g" opensearchproject/opensearch:2.17.0
+
+# Backend
+docker build -t research-backend .
+docker run -d --name research-backend --restart unless-stopped -p 8000:8000 \
+  --add-host=host.docker.internal:host-gateway \
+  -e YACY_URL=http://host.docker.internal:8090 \
+  -e OPENSEARCH_URL=http://host.docker.internal:9200 \
+  -e QDRANT_URL=http://10.30.20.68:6333 \
+  -e OLLAMA_URL=http://10.30.20.186:11434 \
+  research-backend
+```
+
+## Files
+
+- `server.py` — Thin MCP proxy (runs on MacBook)
+- `backend.py` — REST API + Dashboard (runs on CT 145)
+- `docker-compose.yml` — Reference compose file
+- `Dockerfile` — Backend container build
+- `.env` — Service endpoints config
--- a/backend.py
+++ b/backend.py
@@ -0,0 +1,468 @@
+#!/usr/bin/env python3
+"""
+AI Research Engine — Backend API
+Runs on CT 145, handles all heavy lifting.
+Exposes REST API consumed by the MCP proxy (MacBook) and dashboard.
+"""
+
+import os
+import json
+import httpx
+from fastapi import FastAPI, Query, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import HTMLResponse
+import uvicorn
+
+app = FastAPI(title="AI Research Engine Backend")
+app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
+
+# ── Config ──────────────────────────────────────────────────
+YACY_URL       = os.getenv("YACY_URL", "http://localhost:8090")
+OPENSEARCH_URL = os.getenv("OPENSEARCH_URL", "http://localhost:9200")
+QDRANT_URL     = os.getenv("QDRANT_URL", "http://10.30.20.68:6333")
+OLLAMA_URL     = os.getenv("OLLAMA_URL", "http://10.30.20.186:11434")
+OLLAMA_MODEL   = os.getenv("OLLAMA_MODEL", "ornith:latest")
+INDEX_NAME     = os.getenv("INDEX_NAME", "research_docs")
+
+client = httpx.Client(timeout=30.0)
+ollama = httpx.Client(timeout=120.0, base_url=OLLAMA_URL)
+
+# ── Helpers ──────────────────────────────────────────────────
+
+def _ensure_index():
+    try:
+        client.get(f"{OPENSEARCH_URL}/{INDEX_NAME}")
+    except Exception:
+        try:
+            client.put(f"{OPENSEARCH_URL}/{INDEX_NAME}", json={
+                "settings": {"number_of_shards": 1, "number_of_replicas": 0},
+                "mappings": {"properties": {
+                    "url": {"type": "keyword"}, "title": {"type": "text"},
+                    "content": {"type": "text"}, "excerpt": {"type": "text"},
+                    "category": {"type": "keyword"}, "source_domain": {"type": "keyword"},
+                    "crawled_at": {"type": "date"}, "indexed_at": {"type": "date"},
+                    "metadata": {"type": "object"},
+                }}
+            })
+        except Exception:
+            pass
+
+def _ensure_qdrant():
+    try:
+        client.get(f"{QDRANT_URL}/collections/{INDEX_NAME}")
+    except Exception:
+        try:
+            client.put(f"{QDRANT_URL}/collections/{INDEX_NAME}", json={
+                "vectors": {"size": 768, "distance": "Cosine"}
+            })
+        except Exception:
+            pass
+
+def _ai_chat(prompt: str, system: str = "You are a research assistant. Be concise and factual.") -> str:
+    r = ollama.post("/api/chat", json={
+        "model": OLLAMA_MODEL, "messages": [
+            {"role": "system", "content": system},
+            {"role": "user", "content": prompt},
+        ], "stream": False,
+        "options": {"temperature": 0.3, "num_predict": 2048},
+    })
+    body = r.json()
+    return body.get("message", {}).get("content", "") or body.get("thinking", "") or ""
+
+def _get_embedding(text: str) -> list:
+    try:
+        r = ollama.post("/api/embeddings", json={"model": "nomic-embed-text-v2-moe:latest", "prompt": text[:2048]})
+        return r.json().get("embedding", [])
+    except Exception:
+        return []
+
+
+# ── Status ────────────────────────────────────────────────────
+
+@app.get("/health")
+def health():
+    return {"status": "ok"}
+
+@app.get("/api/status")
+def status():
+    svc = {}
+    try:
+        r = client.get(f"{OPENSEARCH_URL}/_cluster/health")
+        cnt = client.post(f"{OPENSEARCH_URL}/{INDEX_NAME}/_count").json() if r.status_code == 200 else {"count": 0}
+        svc["opensearch"] = {"status": r.json().get("status", "down"), "documents": cnt.get("count", 0)}
+    except Exception:
+        svc["opensearch"] = {"status": "down"}
+    try:
+        r = client.get(f"{QDRANT_URL}/healthz")
+        col = client.get(f"{QDRANT_URL}/collections/{INDEX_NAME}").json()
+        svc["qdrant"] = {"status": "ok", "points": col.get("result", {}).get("points_count", 0)}
+    except Exception:
+        svc["qdrant"] = {"status": "down"}
+    try:
+        r = client.get(f"{YACY_URL}/yacysearch.json", params={"query": "test", "maximumRecords": 1})
+        svc["yacy"] = {"status": "ok" if r.status_code == 200 else "down"}
+    except Exception:
+        svc["yacy"] = {"status": "down"}
+    try:
+        r = ollama.get("/api/tags")
+        svc["ollama"] = {"status": "ok", "models": len(r.json().get("models", []))}
+    except Exception:
+        svc["ollama"] = {"status": "down"}
+    return {"services": svc}
+
+
+# ── Search ────────────────────────────────────────────────────
+
+@app.get("/api/search")
+def search_web(q: str = Query(...), category: str = "", limit: int = 10):
+    _ensure_index()
+    body = {
+        "size": limit,
+        "query": {"bool": {"must": [{"multi_match": {"query": q, "fields": ["title^3", "content", "excerpt"]}}]}},
+        "highlight": {"fields": {"content": {"fragment_size": 200, "number_of_fragments": 2}}},
+    }
+    if category:
+        body["query"]["bool"]["filter"] = [{"term": {"category": category}}]
+    try:
+        r = client.post(f"{OPENSEARCH_URL}/{INDEX_NAME}/_search", json=body, params={"refresh": "true"})
+        result = r.json()
+        hits = []
+        for h in result.get("hits", {}).get("hits", []):
+            src = h["_source"]
+            hits.append({"url": src.get("url"), "title": src.get("title"),
+                "excerpt": src.get("excerpt") or (h.get("highlight", {}).get("content", [""])[0]),
+                "category": src.get("category"), "crawled_at": src.get("crawled_at"), "score": h["_score"]})
+        return {"query": q, "total": result.get("hits", {}).get("total", {}).get("value", 0), "hits": hits}
+    except Exception as e:
+        return {"query": q, "total": 0, "hits": [], "note": f"Index may be empty. {e}"}
+
+
+@app.get("/api/semantic-search")
+def semantic_search(q: str = Query(...), limit: int = 10):
+    _ensure_qdrant()
+    emb = _get_embedding(q)
+    if not emb:
+        return {"hits": [], "error": "Embedding model not available"}
+    try:
+        r = client.post(f"{QDRANT_URL}/collections/{INDEX_NAME}/points/search", json={
+            "vector": emb, "limit": limit, "with_payload": True, "with_vector": False})
+        hits = [{"url": p.get("payload", {}).get("url"), "title": p.get("payload", {}).get("title"),
+            "excerpt": str(p.get("payload", {}).get("excerpt", ""))[:300], "score": p.get("score")}
+            for p in r.json().get("result", [])]
+        return {"query": q, "total": len(hits), "hits": hits}
+    except Exception as e:
+        return {"hits": [], "error": str(e)}
+
+
+# ── Crawl ─────────────────────────────────────────────────────
+
+@app.get("/api/crawl")
+def crawl_url(url: str = Query(...), depth: int = 1):
+    try:
+        r = client.get(f"{YACY_URL}/Crawler_p.json", params={
+            "crawlingDomMaxPages": 50, "crawlingDepth": depth,
+            "crawlingStart": url, "crawlingQ": "on",
+            "bookmarkTitle": "research", "bookmarkFolder": "/research",
+            "indexText": "on", "indexMedia": "on",
+            "crawlingMode": "url", "cachePolicy": "iffresh",
+        })
+        return {"status": "crawl_started", "url": url, "depth": depth}
+    except Exception as e:
+        return {"status": "error", "url": url, "error": str(e)}
+
+
+@app.get("/api/crawl-topic")
+def crawl_topic(topic: str = Query(...), max_urls: int = 20):
+    discovered = []
+    try:
+        r = client.get(f"{YACY_URL}/yacysearch.json", params={"query": topic, "maximumRecords": max_urls, "resource": "global"})
+        for ch in r.json().get("channels", []):
+            for item in ch.get("items", []):
+                if item.get("link"):
+                    discovered.append({"url": item["link"], "title": item.get("title", ""), "snippet": item.get("description", "")})
+    except Exception as e:
+        return {"topic": topic, "error": f"Discovery failed: {e}", "urls_discovered": 0, "urls_crawled": 0}
+
+    crawled = 0
+    for u in discovered[:max_urls]:
+        try:
+            client.get(f"{YACY_URL}/Crawler_p.json", params={
+                "crawlingDomMaxPages": 10, "crawlingDepth": 0,
+                "crawlingStart": u["url"], "crawlingQ": "on",
+                "indexText": "on", "indexMedia": "on",
+                "crawlingMode": "url", "cachePolicy": "iffresh",
+            }, timeout=5.0)
+            crawled += 1
+        except Exception:
+            pass
+
+    return {"topic": topic, "urls_discovered": len(discovered), "urls_crawled": crawled}
+
+
+# ── Document Retrieval ────────────────────────────────────────
+
+@app.get("/api/document")
+def retrieve_document(url: str = Query(...)):
+    try:
+        r = client.post(f"{OPENSEARCH_URL}/{INDEX_NAME}/_search", json={
+            "size": 1, "query": {"term": {"url": url}}})
+        hits = r.json().get("hits", {}).get("hits", [])
+        if not hits:
+            return {"error": "Not found", "url": url}
+        src = hits[0]["_source"]
+        return {"url": src.get("url"), "title": src.get("title"),
+            "content": src.get("content", "")[:10000], "excerpt": src.get("excerpt"),
+            "category": src.get("category"), "crawled_at": src.get("crawled_at")}
+    except Exception as e:
+        return {"error": str(e), "url": url}
+
+
+# ── AI Synthesis ──────────────────────────────────────────────
+
+@app.get("/api/summarize")
+def summarize_sources(urls: str = Query(...), instruction: str = "Summarize key points"):
+    url_list = [u.strip() for u in urls.split(",") if u.strip()]
+    combined = ""
+    for url in url_list[:5]:
+        try:
+            r = client.post(f"{OPENSEARCH_URL}/{INDEX_NAME}/_search", json={"size": 1, "query": {"term": {"url": url}}})
+            hits = r.json().get("hits", {}).get("hits", [])
+            if hits:
+                src = hits[0]["_source"]
+                combined += f"\n\n--- {url} ---\n{src.get('title','')}\n{src.get('content', src.get('excerpt',''))[:2000]}"
+        except Exception:
+            pass
+    if not combined.strip():
+        return {"error": "No content retrieved"}
+    summary = _ai_chat(f"Instruction: {instruction}\n\nSources:{combined}\n\nProvide a structured summary.")
+    return {"instruction": instruction, "sources": len(url_list), "summary": summary}
+
+
+@app.get("/api/extract")
+def extract_information(url: str = Query(...), schema: str = Query("company names, products, prices")):
+    try:
+        r = client.post(f"{OPENSEARCH_URL}/{INDEX_NAME}/_search", json={"size": 1, "query": {"term": {"url": url}}})
+        hits = r.json().get("hits", {}).get("hits", [])
+        if not hits:
+            return {"error": "Not found", "url": url}
+        content = hits[0]["_source"].get("content", "")[:8000]
+        prompt = f"Extract: {schema}\n\nDocument:\n{content}\n\nReturn ONLY valid JSON."
+        result = _ai_chat(prompt, system="Extract structured data. Return ONLY valid JSON. No explanation.")
+        return {"url": url, "schema": schema, "extracted": result}
+    except Exception as e:
+        return {"error": str(e)}
+
+
+@app.get("/api/report")
+def create_report(topic: str = Query(...), sources: str = ""):
+    if sources:
+        urls = [u.strip() for u in sources.split(",") if u.strip()]
+    else:
+        try:
+            r = client.post(f"{OPENSEARCH_URL}/{INDEX_NAME}/_search", json={
+                "size": 8, "query": {"multi_match": {"query": topic, "fields": ["title^3", "content"]}}})
+            urls = [h["_source"]["url"] for h in r.json().get("hits", {}).get("hits", [])]
+        except Exception:
+            urls = []
+
+    gathered = ""
+    for url in urls[:8]:
+        try:
+            r = client.post(f"{OPENSEARCH_URL}/{INDEX_NAME}/_search", json={"size": 1, "query": {"term": {"url": url}}})
+            hits = r.json().get("hits", {}).get("hits", [])
+            if hits:
+                src = hits[0]["_source"]
+                gathered += f"\n\n### {src.get('title','Source')}\nURL: {url}\n{src.get('content',src.get('excerpt',''))[:1500]}"
+        except Exception:
+            pass
+
+    prompt = f"""Research topic: {topic}\nSources:{gathered if gathered else ' No sources found.'}
+
+Generate a comprehensive report:
+1. Executive Summary
+2. Key Findings (numbered)
+3. Source Analysis
+4. Knowledge Gaps
+5. Recommendations
+
+Be thorough, use markdown, cite sources."""
+    report = _ai_chat(prompt, system="You are a senior research analyst. Produce thorough, structured reports.")
+    return {"topic": topic, "sources_used": len(urls), "report": report}
+
+
+# ── Research Pipeline ─────────────────────────────────────────
+
+@app.get("/api/research")
+def research_topic(topic: str = Query(...)):
+    steps = []
+    kw_result = {"hits": []}
+    sem_result = {"hits": []}
+
+    try:
+        r = client.post(f"{OPENSEARCH_URL}/{INDEX_NAME}/_search", json={
+            "size": 5, "query": {"multi_match": {"query": topic, "fields": ["title^3", "content", "excerpt"]}},
+            "highlight": {"fields": {"content": {"fragment_size": 200, "number_of_fragments": 1}}},
+        })
+        hits = [{"title": h["_source"].get("title"), "excerpt": h.get("highlight", {}).get("content", [h["_source"].get("excerpt", "")])[0]}
+                for h in r.json().get("hits", {}).get("hits", [])]
+        kw_result = {"total": len(hits), "hits": hits}
+        steps.append("keyword_search_done")
+    except Exception:
+        steps.append("keyword_search_skipped")
+
+    try:
+        emb = _get_embedding(topic)
+        if emb:
+            r = client.post(f"{QDRANT_URL}/collections/{INDEX_NAME}/points/search", json={
+                "vector": emb, "limit": 5, "with_payload": True})
+            hits2 = [{"title": p.get("payload", {}).get("title", ""), "excerpt": str(p.get("payload", {}).get("excerpt", ""))[:200],
+                "score": p.get("score")} for p in r.json().get("result", [])]
+            sem_result = {"total": len(hits2), "hits": hits2}
+            steps.append("semantic_search_done")
+    except Exception:
+        steps.append("semantic_search_skipped")
+
+    try:
+        r = client.get(f"{YACY_URL}/yacysearch.json", params={"query": topic, "maximumRecords": 10, "resource": "global"})
+        discovered = [item.get("link") for ch in r.json().get("channels", []) for item in ch.get("items", []) if item.get("link")]
+        for url in discovered[:10]:
+            try:
+                client.get(f"{YACY_URL}/Crawler_p.json", params={
+                    "crawlingDomMaxPages": 10, "crawlingDepth": 0, "crawlingStart": url,
+                    "crawlingQ": "on", "indexText": "on", "indexMedia": "on",
+                    "crawlingMode": "url", "cachePolicy": "iffresh"}, timeout=5.0)
+            except Exception:
+                pass
+        steps.append(f"crawl_dispatched_{len(discovered[:10])}")
+    except Exception:
+        steps.append("crawl_skipped")
+
+    all_sources = ""
+    for h in kw_result.get("hits", [])[:3] + sem_result.get("hits", [])[:3]:
+        all_sources += f"- {h.get('title', 'Unknown')}: {h.get('excerpt', '')[:200]}\n"
+
+    summary = ""
+    if all_sources:
+        summary = _ai_chat(
+            f"Research topic: {topic}\n\nSources:\n{all_sources}\n\nConcise research summary (3-5 paragraphs): "
+            "key findings, important sources, knowledge gaps, next steps.")
+
+    return {"topic": topic, "steps": steps,
+        "keyword_results": kw_result, "semantic_results": sem_result,
+        "ai_summary": summary}
+
+
+# ── Dashboard ─────────────────────────────────────────────────
+
+@app.get("/", response_class=HTMLResponse)
+def dashboard():
+    return HTMLResponse("""
+<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"><meta name="viewport" content="width=device-width,initial-scale=1.0">
+<title>AI Research Engine</title><link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700;800&display=swap" rel="stylesheet">
+<style>
+:root{--bg:#09090b;--surface:#18181b;--border:#27272a;--accent:#7c3aed;--accent2:#a855f7;--text:#f4f4f5;--muted:#a1a1aa;--green:#22c55e;--red:#ef4444;--amber:#f59e0b}
+*{box-sizing:border-box;margin:0;padding:0}
+body{font-family:'Inter',system-ui,sans-serif;background:var(--bg);color:var(--text);min-height:100vh}
+.header{background:var(--surface);border-bottom:1px solid var(--border);padding:20px 32px;display:flex;justify-content:space-between;align-items:center;position:sticky;top:0;z-index:10}
+.header h1{font-size:1.4rem;font-weight:800;background:linear-gradient(135deg,var(--accent),var(--accent2),#ec4899);-webkit-background-clip:text;-webkit-text-fill-color:transparent;letter-spacing:-0.02em}
+.header .badge{font-size:0.75rem;color:var(--muted);background:var(--border);padding:6px 12px;border-radius:999px}
+.container{max-width:1400px;margin:0 auto;padding:32px 24px}
+.hero{text-align:center;padding:48px 0 32px}
+.hero h2{font-size:2.2rem;font-weight:800;letter-spacing:-0.03em;margin-bottom:12px}
+.hero p{color:var(--muted);font-size:1.1rem;max-width:600px;margin:0 auto}
+.search-box{display:flex;gap:12px;max-width:800px;margin:0 auto 40px}
+.search-box input{flex:1;padding:16px 24px;border-radius:16px;border:1px solid var(--border);background:var(--surface);color:var(--text);font-size:1rem;outline:none;transition:border-color .2s}
+.search-box input:focus{border-color:var(--accent)}
+.search-box button{padding:16px 32px;border-radius:16px;border:none;font-weight:600;font-size:1rem;cursor:pointer;transition:all .2s}
+.btn-primary{background:var(--accent);color:white}
+.btn-primary:hover{background:var(--accent2)}
+.btn-secondary{background:var(--surface);color:var(--text);border:1px solid var(--border)}
+.btn-secondary:hover{border-color:var(--accent)}
+.grid{display:grid;grid-template-columns:repeat(auto-fit,minmax(260px,1fr));gap:16px;margin-bottom:32px}
+.card{background:var(--surface);border:1px solid var(--border);border-radius:16px;padding:24px;transition:border-color .2s}
+.card:hover{border-color:var(--accent)}
+.card .label{color:var(--muted);font-size:0.8rem;text-transform:uppercase;letter-spacing:0.05em;margin-bottom:8px}
+.card .value{font-size:1.8rem;font-weight:700}
+.card .sub{color:var(--muted);font-size:0.85rem;margin-top:4px}
+.ok{color:var(--green)}.down{color:var(--red)}.warn{color:var(--amber)}
+.action-bar{display:flex;gap:10px;margin-bottom:28px;flex-wrap:wrap}
+.action-bar button{padding:10px 20px;border-radius:10px;border:1px solid var(--border);background:var(--surface);color:var(--text);cursor:pointer;font-size:0.9rem;transition:all .15s}
+.action-bar button:hover{border-color:var(--accent);background:#1f1f23}
+.results{display:flex;flex-direction:column;gap:12px}
+.result-card{background:var(--surface);border:1px solid var(--border);border-radius:14px;padding:20px;transition:border-color .15s}
+.result-card:hover{border-color:var(--accent)}
+.result-card h3{margin-bottom:6px;font-size:1.1rem}
+.result-card h3 a{color:var(--accent2);text-decoration:none}
+.result-card h3 a:hover{text-decoration:underline}
+.result-card .excerpt{color:var(--muted);font-size:0.9rem;line-height:1.5}
+.result-card .meta{color:#71717a;font-size:0.78rem;margin-top:10px;display:flex;gap:16px}
+.loading{text-align:center;padding:48px;color:var(--muted)}
+.hidden{display:none}
+pre{background:var(--surface);border:1px solid var(--border);border-radius:12px;padding:20px;overflow-x:auto;font-size:0.85rem;line-height:1.6;white-space:pre-wrap}
+.report{background:var(--surface);border:1px solid var(--border);border-radius:14px;padding:24px;line-height:1.7}
+.report h1,.report h2,.report h3{color:var(--accent2);margin:16px 0 8px}
+.report ul,.report ol{padding-left:20px;margin:8px 0}
+.report li{margin:4px 0}
+</style></head><body>
+<div class="header"><h1>🧠 AI Research Engine</h1><div class="badge">Self-hosted · Private</div></div>
+<div class="container">
+<div class="hero"><h2>Your Private Research Cloud</h2><p>Discover, index, and synthesize knowledge — all on your own infrastructure.</p></div>
+<div class="search-box">
+<input type="text" id="query" placeholder="Research anything..." onkeydown="if(event.key==='Enter')search()">
+<button class="btn-primary" onclick="search()">🔍 Search</button>
+<button class="btn-secondary" onclick="research()" style="background:var(--accent);color:white;">🧪 Deep Research</button>
+</div>
+<div class="action-bar">
+<button onclick="loadStatus()">📡 System Status</button>
+<button onclick="toggleCrawl()">🕷️ Crawl URL</button>
+<button onclick="loadReport()">📄 Generate Report</button>
+</div>
+<div id="crawl-box" class="hidden" style="margin-bottom:20px;display:flex;gap:10px;">
+<input type="text" id="crawl-input" placeholder="https://example.com" style="flex:1;padding:12px 16px;border-radius:12px;border:1px solid var(--border);background:var(--surface);color:var(--text);">
+<button class="btn-primary" onclick="crawl()">Crawl</button>
+</div>
+<div class="grid" id="stats"></div>
+<div id="output"></div>
+</div>
+<script>
+async function api(p){const r=await fetch(p);return r.json()}
+async function loadStatus(){
+try{const d=await api('/api/status');let h='';for(const[n,i]of Object.entries(d.services||{})){
+const c=i.status==='ok'||i.status==='green'?'ok':'down';
+let x='';if(i.documents!==undefined)x=i.documents+' docs';if(i.points!==undefined)x=i.points+' vectors';if(i.models!==undefined)x=i.models+' models';
+h+=`<div class="card"><div class="label">${n}</div><div class="value ${c}">${i.status||'down'}</div><div class="sub">${x||'—'}</div></div>`}
+document.getElementById('stats').innerHTML=h}catch(e){}
+}
+async function search(){
+const q=document.getElementById('query').value;if(!q)return;
+document.getElementById('output').innerHTML='<div class="loading">Searching...</div>';
+const d=await api('/api/search?q='+encodeURIComponent(q));
+let h=`<p style="color:var(--muted);margin-bottom:16px">${d.total||0} results for "${d.query||q}"</p><div class="results">`;
+for(const r of(d.hits||[]))h+=`<div class="result-card"><h3><a href="${r.url||'#'}" target="_blank">${r.title||'Untitled'}</a></h3><div class="excerpt">${r.excerpt||''}</div><div class="meta"><span>⭐ ${(r.score||0).toFixed(1)}</span>${r.category?`<span>📁 ${r.category}</span>`:''}<span>${r.crawled_at||''}</span></div></div>`;
+h+='</div>';document.getElementById('output').innerHTML=h}
+async function research(){
+const q=document.getElementById('query').value;if(!q)return;
+document.getElementById('output').innerHTML='<div class="loading">Deep researching... this may take 30-60 seconds</div>';
+const d=await api('/api/research?topic='+encodeURIComponent(q));
+let h=`<div class="card" style="margin-bottom:16px"><div class="label">Research Complete</div><div class="value" style="font-size:1.2rem">${d.topic}</div><div class="sub">Steps: ${(d.steps||[]).join(' → ')}</div></div>`;
+if(d.ai_summary)h+=`<div class="report"><h2>AI Summary</h2>${d.ai_summary.replace(/\\n/g,'<br>')}</div>`;
+h+=`<div style="display:grid;grid-template-columns:1fr 1fr;gap:16px;margin-top:16px">`;
+h+=`<div class="card"><div class="label">Keyword Results</div><div class="sub">${d.keyword_results?.total||0} hits</div></div>`;
+h+=`<div class="card"><div class="label">Semantic Results</div><div class="sub">${d.semantic_results?.total||0} hits</div></div></div>`;
+document.getElementById('output').innerHTML=h}
+function toggleCrawl(){document.getElementById('crawl-box').classList.toggle('hidden')}
+async function crawl(){
+const u=document.getElementById('crawl-input').value;if(!u)return;
+document.getElementById('output').innerHTML='<div class="loading">Dispatching crawl...</div>';
+const d=await api('/api/crawl?url='+encodeURIComponent(u));
+document.getElementById('output').innerHTML='<pre>'+JSON.stringify(d,null,2)+'</pre>'}
+async function loadReport(){
+const q=document.getElementById('query').value||'AI research';
+document.getElementById('output').innerHTML='<div class="loading">Generating report...</div>';
+const d=await api('/api/report?topic='+encodeURIComponent(q));
+document.getElementById('output').innerHTML=`<div class="report"><h2>📊 Research Report: ${d.topic}</h2>${(d.report||'').replace(/\\n/g,'<br>')}<p style="color:var(--muted);margin-top:16px">Sources: ${d.sources_used||0}</p></div>`}
+loadStatus()
+</script></body></html>""")
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)
--- a/dashboard.py
+++ b/dashboard.py
@@ -0,0 +1,226 @@
+#!/usr/bin/env python3
+"""
+Research Engine Dashboard — FastAPI web UI
+Runs on CT 145 port 8000
+"""
+
+import os
+import json
+import httpx
+from fastapi import FastAPI, Request, Query
+from fastapi.responses import HTMLResponse, JSONResponse
+from fastapi.staticfiles import StaticFiles
+from dotenv import load_dotenv
+
+load_dotenv(os.path.expanduser("~/ai-research-engine/.env"))
+
+app = FastAPI(title="AI Research Engine")
+
+OPENSEARCH_URL = os.getenv("OPENSEARCH_URL", "http://localhost:9200")
+QDRANT_URL = os.getenv("QDRANT_URL", "http://10.30.20.68:6333")
+YACY_URL = os.getenv("YACY_URL", "http://localhost:8090")
+INDEX_NAME = "research_docs"
+
+client = httpx.Client(timeout=10.0)
+
+@app.get("/", response_class=HTMLResponse)
+async def dashboard():
+    return HTMLResponse("""
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>AI Research Engine</title>
+<style>
+  :root { --bg: #0f0f0f; --card: #1a1a1a; --accent: #6366f1; --text: #e2e8f0; --muted: #94a3b8; --green: #22c55e; --red: #ef4444; }
+  * { box-sizing: border-box; margin: 0; padding: 0; }
+  body { font-family: 'Inter', -apple-system, sans-serif; background: var(--bg); color: var(--text); min-height: 100vh; }
+  .header { background: var(--card); border-bottom: 1px solid #262626; padding: 20px 40px; display: flex; justify-content: space-between; align-items: center; }
+  .header h1 { font-size: 1.5rem; font-weight: 700; background: linear-gradient(135deg, var(--accent), #a855f7); -webkit-background-clip: text; -webkit-text-fill-color: transparent; }
+  .container { max-width: 1200px; margin: 0 auto; padding: 30px 20px; }
+  .search-box { display: flex; gap: 10px; margin-bottom: 30px; }
+  .search-box input { flex: 1; padding: 14px 20px; border-radius: 12px; border: 1px solid #333; background: var(--card); color: var(--text); font-size: 1rem; }
+  .search-box button { padding: 14px 28px; border-radius: 12px; border: none; background: var(--accent); color: white; font-weight: 600; cursor: pointer; font-size: 1rem; }
+  .search-box button:hover { opacity: 0.9; }
+  .grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(280px, 1fr)); gap: 16px; margin-bottom: 30px; }
+  .stat-card { background: var(--card); border: 1px solid #262626; border-radius: 12px; padding: 20px; }
+  .stat-card .label { color: var(--muted); font-size: 0.85rem; margin-bottom: 4px; }
+  .stat-card .value { font-size: 1.8rem; font-weight: 700; }
+  .stat-card .ok { color: var(--green); } .stat-card .down { color: var(--red); }
+  .results { display: flex; flex-direction: column; gap: 12px; }
+  .result-card { background: var(--card); border: 1px solid #262626; border-radius: 12px; padding: 20px; }
+  .result-card h3 { margin-bottom: 6px; }
+  .result-card h3 a { color: var(--accent); text-decoration: none; }
+  .result-card .excerpt { color: var(--muted); font-size: 0.9rem; line-height: 1.5; }
+  .result-card .meta { color: #64748b; font-size: 0.8rem; margin-top: 8px; }
+  .action-bar { display: flex; gap: 8px; margin-bottom: 20px; flex-wrap: wrap; }
+  .action-bar button { padding: 10px 20px; border-radius: 8px; border: 1px solid #333; background: var(--card); color: var(--text); cursor: pointer; font-size: 0.9rem; }
+  .action-bar button:hover { border-color: var(--accent); }
+  .loading { text-align: center; padding: 40px; color: var(--muted); }
+</style>
+</head>
+<body>
+  <div class="header">
+    <h1>🧠 AI Research Engine</h1>
+    <span style="color:var(--muted);font-size:0.85rem">Self-hosted knowledge acquisition</span>
+  </div>
+  <div class="container">
+    <div class="search-box">
+      <input type="text" id="query" placeholder="Research anything..." onkeydown="if(event.key==='Enter')search()">
+      <button onclick="search()">Search</button>
+      <button onclick="research()" style="background:#a855f7;">Deep Research</button>
+    </div>
+    <div class="action-bar">
+      <button onclick="loadStatus()">📊 System Status</button>
+      <button onclick="document.getElementById('crawl-url').style.display='block'">🕷️ Crawl URL</button>
+    </div>
+    <div id="crawl-url" style="display:none;margin-bottom:20px;display:none;">
+      <input type="text" id="crawl-input" placeholder="https://example.com" style="padding:10px;width:400px;border-radius:8px;border:1px solid #333;background:var(--card);color:var(--text);">
+      <button onclick="crawl()" style="padding:10px 20px;border-radius:8px;border:none;background:var(--accent);color:white;cursor:pointer;margin-left:8px;">Crawl</button>
+    </div>
+    <div class="grid" id="stats"></div>
+    <div id="output"></div>
+  </div>
+<script>
+async function api(path) { const r = await fetch(path); return r.json(); }
+async function loadStatus() {
+  try { const d = await api('/api/status'); renderStatus(d); } catch(e) { document.getElementById('output').innerHTML='<div class="loading">Backend unreachable — is docker-compose running?</div>'; }
+}
+async function search() {
+  const q = document.getElementById('query').value;
+  if(!q) return;
+  document.getElementById('output').innerHTML='<div class="loading">Searching...</div>';
+  try { const d = await api('/api/search?q='+encodeURIComponent(q)); renderResults(d); } catch(e) { document.getElementById('output').innerHTML='<div class="loading">Error: '+e.message+'</div>'; }
+}
+async function research() {
+  const q = document.getElementById('query').value;
+  if(!q) return;
+  document.getElementById('output').innerHTML='<div class="loading">Researching (this may take a minute)...</div>';
+  try { const d = await api('/api/research?topic='+encodeURIComponent(q)); renderResearch(d); } catch(e) { document.getElementById('output').innerHTML='<div class="loading">Error: '+e.message+'</div>'; }
+}
+async function crawl() {
+  const url = document.getElementById('crawl-input').value;
+  if(!url) return;
+  document.getElementById('output').innerHTML='<div class="loading">Crawling...</div>';
+  try { const d = await api('/api/crawl?url='+encodeURIComponent(url)); document.getElementById('output').innerHTML='<pre style="background:var(--card);padding:16px;border-radius:8px;overflow-x:auto;">'+JSON.stringify(d,null,2)+'</pre>'; } catch(e) {}
+}
+function renderStatus(d) {
+  let html = '';
+  for(const [name, info] of Object.entries(d.services||{})) {
+    const cls = info.status==='ok'||info.status==='green'?'ok':'down';
+    html += `<div class="stat-card"><div class="label">${name}</div><div class="value ${cls}">${info.status||'down'}</div><div style="color:var(--muted);font-size:0.8rem;margin-top:4px;">${info.documents!==undefined?'📄 '+info.documents+' docs':''}${info.points!==undefined?'🔢 '+info.points+' vectors':''}</div></div>`;
+  }
+  document.getElementById('stats').innerHTML = html;
+}
+function renderResults(d) {
+  let html = `<p style="color:var(--muted);margin-bottom:16px;">Found ${d.total||0} results for "${d.query||''}"</p><div class="results">`;
+  for(const h of (d.hits||[])) {
+    html += `<div class="result-card"><h3><a href="${h.url||'#'}" target="_blank">${h.title||'Untitled'}</a></h3><div class="excerpt">${h.excerpt||''}</div><div class="meta">Score: ${(h.score||0).toFixed(2)} · ${h.category||''} · ${h.crawled_at||''}</div></div>`;
+  }
+  html += '</div>';
+  document.getElementById('output').innerHTML = html;
+}
+function renderResearch(d) {
+  let html = `<div class="result-card"><h3>Research: ${d.topic||''}</h3>`;
+  if(d.ai_summary) html += `<div class="excerpt" style="white-space:pre-wrap;">${d.ai_summary}</div>`;
+  html += `<div class="meta">Steps: ${(d.steps||[]).join(', ')}</div></div>`;
+  document.getElementById('output').innerHTML = html;
+}
+loadStatus();
+</script>
+</body>
+</html>
+""")
+
+@app.get("/api/status")
+async def status():
+    svc = {}
+    # OpenSearch
+    try:
+        r = client.get(f"{OPENSEARCH_URL}/_cluster/health")
+        h = r.json()
+        cnt = client.post(f"{OPENSEARCH_URL}/{INDEX_NAME}/_count").json()
+        svc["opensearch"] = {"status": h.get("status"), "documents": cnt.get("count", 0)}
+    except Exception as e:
+        svc["opensearch"] = {"status": "down", "error": str(e)}
+    # Qdrant
+    try:
+        r = client.get(f"{QDRANT_URL}/health")
+        if r.status_code == 200:
+            col = client.get(f"{QDRANT_URL}/collections/{INDEX_NAME}").json()
+            svc["qdrant"] = {"status": "ok", "points": col.get("result", {}).get("points_count", 0)}
+    except Exception as e:
+        svc["qdrant"] = {"status": "down", "error": str(e)}
+    # YaCy
+    try:
+        r = client.get(f"{YACY_URL}/api/status.json")
+        svc["yacy"] = {"status": r.json().get("status", "unknown")}
+    except Exception as e:
+        svc["yacy"] = {"status": "down", "error": str(e)}
+    # Ollama
+    try:
+        r = httpx.get("http://10.30.20.186:11434/api/tags", timeout=5.0)
+        svc["ollama"] = {"status": "ok", "models": len(r.json().get("models", []))}
+    except Exception as e:
+        svc["ollama"] = {"status": "down", "error": str(e)}
+
+    return {"services": svc}
+
+@app.get("/api/search")
+async def search(q: str = Query(...), limit: int = 10):
+    try:
+        r = client.post(f"{OPENSEARCH_URL}/{INDEX_NAME}/_search", json={
+            "size": limit,
+            "query": {"multi_match": {"query": q, "fields": ["title^3", "content", "excerpt"]}},
+            "highlight": {"fields": {"content": {"fragment_size": 200, "number_of_fragments": 2}}},
+        })
+        hits = []
+        for h in r.json().get("hits", {}).get("hits", []):
+            src = h["_source"]
+            hits.append({
+                "url": src.get("url"), "title": src.get("title"),
+                "excerpt": h.get("highlight", {}).get("content", [src.get("excerpt", "")])[0],
+                "category": src.get("category"), "crawled_at": src.get("crawled_at"),
+                "score": h["_score"],
+            })
+        return {"query": q, "total": r.json().get("hits", {}).get("total", {}).get("value", 0), "hits": hits}
+    except Exception as e:
+        return {"query": q, "total": 0, "hits": [], "error": str(e)}
+
+@app.get("/api/crawl")
+async def crawl(url: str = Query(...)):
+    try:
+        r = client.get(f"{YACY_URL}/Crawler_p.json", params={
+            "crawlingDomMaxPages": 50, "crawlingDepth": 1,
+            "crawlingStart": url, "crawlingQ": "on",
+            "bookmarkTitle": "research", "bookmarkFolder": "/research",
+            "indexText": "on", "indexMedia": "on",
+            "crawlingMode": "url", "cachePolicy": "iffresh",
+        })
+        return {"status": "crawl_started", "url": url, "yacy": r.json()}
+    except Exception as e:
+        return {"status": "error", "url": url, "error": str(e)}
+
+@app.get("/api/research")
+async def research(topic: str = Query(...)):
+    # Proxy to MCP server's research_topic via command
+    import subprocess, sys
+    result = subprocess.run(
+        [sys.executable, "-c", f"""
+import json, sys
+sys.path.insert(0, '/Users/drjones/ai-research-engine')
+from server import research_topic
+print(research_topic("{topic}"))
+"""],
+        capture_output=True, text=True, timeout=120,
+        env={**os.environ, "PYTHONPATH": "/Users/drjones/ai-research-engine"}
+    )
+    try:
+        return json.loads(result.stdout)
+    except:
+        return {"error": result.stderr, "topic": topic}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -0,0 +1,62 @@
+version: '3.8'
+
+services:
+  # ── Web Crawler ────────────────────────────────────────────
+  yacy:
+    image: yacy/yacy_search_server:latest
+    container_name: yacy
+    ports:
+      - "8090:8090"
+    environment:
+      - YACY_ADMIN_PASSWORD=research2026
+    volumes:
+      - yacy_data:/opt/yacy_search_server/DATA
+    restart: unless-stopped
+    mem_limit: 2g
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8090/api/status.json"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+
+  # ── Document Index ─────────────────────────────────────────
+  opensearch:
+    image: opensearchproject/opensearch:2.17.0
+    container_name: opensearch
+    environment:
+      - discovery.type=single-node
+      - DISABLE_SECURITY_PLUGIN=true
+      - "OPENSEARCH_JAVA_OPTS=-Xms1g -Xmx2g"
+      - DISABLE_INSTALL_DEMO_CONFIG=true
+    ports:
+      - "9200:9200"
+      - "9600:9600"
+    volumes:
+      - opensearch_data:/usr/share/opensearch/data
+    restart: unless-stopped
+    mem_limit: 3g
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:9200/_cluster/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 5
+
+  # ── Cache / Job Queue ──────────────────────────────────────
+  redis:
+    image: redis:7-alpine
+    container_name: redis
+    ports:
+      - "6379:6379"
+    volumes:
+      - redis_data:/data
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "redis-cli", "ping"]
+      interval: 10s
+      timeout: 5s
+      retries: 3
+
+volumes:
+  yacy_data:
+  opensearch_data:
+  redis_data:
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -0,0 +1,9 @@
+[project]
+name = "ai-research-engine"
+version = "0.1.0"
+requires-python = ">=3.10"
+dependencies = [
+    "mcp>=1.0.0,<2.0.0",
+    "httpx>=0.27.0",
+    "python-dotenv>=1.0.0",
+]
--- a/server.py
+++ b/server.py
@@ -0,0 +1,110 @@
+#!/usr/bin/env python3
+"""
+AI Research Engine — Thin MCP Proxy
+Runs on MacBook. Forwards all tool calls to the CT 145 backend.
+Minimal resource usage — all heavy lifting on Proxmox.
+"""
+
+import json
+import httpx
+from mcp.server import FastMCP
+
+BACKEND_URL = "http://10.30.20.249:8000"
+client = httpx.Client(timeout=120.0)
+
+mcp = FastMCP(
+    "ai-research-engine",
+    instructions="""
+AI Research Engine — private knowledge acquisition system.
+
+search_web(query) — Full-text search across indexed documents
+semantic_search(query) — Find documents by meaning (vector search)
+crawl_url(url) — Crawl a URL into the index
+crawl_topic(topic) — Discover and crawl sources for a topic
+research_topic(topic) — Full pipeline: discover → crawl → summarize
+retrieve_document(url) — Get full content of an indexed document
+summarize_sources(urls, instruction) — AI summary of multiple sources
+extract_information(url, schema) — Structured data extraction
+create_report(topic, sources) — Generate comprehensive research report
+index_status() — System health and stats
+""",
+)
+
+def _get(path: str) -> dict:
+    r = client.get(f"{BACKEND_URL}{path}")
+    r.raise_for_status()
+    return r.json()
+
+
+@mcp.tool()
+def search_web(query: str, category: str = "", limit: int = 10) -> str:
+    """Full-text search across indexed documents. Find by keywords, titles, content."""
+    r = _get(f"/api/search?q={query}&category={category}&limit={limit}")
+    return json.dumps(r, indent=2)
+
+
+@mcp.tool()
+def semantic_search(query: str, limit: int = 10) -> str:
+    """Search by meaning using vector embeddings. Finds conceptually related docs."""
+    r = _get(f"/api/semantic-search?q={query}&limit={limit}")
+    return json.dumps(r, indent=2)
+
+
+@mcp.tool()
+def crawl_url(url: str, depth: int = 1) -> str:
+    """Crawl a URL. depth: 0=just this page, 1=+linked pages."""
+    r = _get(f"/api/crawl?url={url}&depth={depth}")
+    return json.dumps(r, indent=2)
+
+
+@mcp.tool()
+def crawl_topic(topic: str, max_urls: int = 20) -> str:
+    """Discover and crawl sources for a topic using YaCy."""
+    r = _get(f"/api/crawl-topic?topic={topic}&max_urls={max_urls}")
+    return json.dumps(r, indent=2)
+
+
+@mcp.tool()
+def research_topic(topic: str) -> str:
+    """Full research pipeline: keyword search → semantic search → crawl new sources → AI summary."""
+    r = _get(f"/api/research?topic={topic}")
+    return json.dumps(r, indent=2)
+
+
+@mcp.tool()
+def retrieve_document(url: str) -> str:
+    """Get full indexed content of a document by URL."""
+    r = _get(f"/api/document?url={url}")
+    return json.dumps(r, indent=2)
+
+
+@mcp.tool()
+def summarize_sources(urls: str, instruction: str = "Summarize key points") -> str:
+    """Summarize multiple URLs using local LLM. urls: comma-separated."""
+    r = _get(f"/api/summarize?urls={urls}&instruction={instruction}")
+    return json.dumps(r, indent=2)
+
+
+@mcp.tool()
+def extract_information(url: str, schema: str = "company names, products, prices, specifications") -> str:
+    """Extract structured information from a document using LLM."""
+    r = _get(f"/api/extract?url={url}&schema={schema}")
+    return json.dumps(r, indent=2)
+
+
+@mcp.tool()
+def create_report(topic: str, sources: str = "") -> str:
+    """Generate a comprehensive research report. sources: optional comma-separated URLs."""
+    r = _get(f"/api/report?topic={topic}&sources={sources}")
+    return json.dumps(r, indent=2)
+
+
+@mcp.tool()
+def index_status() -> str:
+    """Check health of all backend services: OpenSearch, Qdrant, YaCy, Ollama."""
+    r = _get("/api/status")
+    return json.dumps(r, indent=2)
+
+
+if __name__ == "__main__":
+    mcp.run(transport="stdio")