ThinkSplitter v3: narration gate + paragraph classifier + degenerate-output auto-recovery; num_predict 1100

mcp_server.py

@@ -0,0 +1,54 @@
+#!/usr/bin/env python3
+"""DRACO MCP server — standalone streamable-http ASGI app on :8013.
+Run:  uvicorn mcp_server:app --host 127.0.0.1 --port 8013   (or python3 mcp_server.py)
+nginx routes /mcp here; Flask keeps :8012.
+"""
+import os
+
+from mcp.server.fastmcp import FastMCP
+
+import draco_core as core
+from draco_core import CFG
+
+MCP_PORT = int(os.environ.get("DRACO_MCP_PORT", 8013))
+
+mcp = FastMCP("draco", host="127.0.0.1", port=MCP_PORT, streamable_http_path="/mcp")
+
+
+@mcp.tool()
+def draco_ask(question: str) -> str:
+    """Ask DRACO a coding or security question. Answers are grounded in hundreds of
+    real programming and hacking books, with [n] citations and source titles."""
+    prompt, hits = core.build_prompt(question, k=CFG.get("rag_k", 6))
+    try:
+        answer = core.ask_ollama(prompt)
+    except Exception as e:
+        return f"Model offline: {e}"
+    src = "\n".join(f"[{i+1}] {h['title']} ({h['category']})" for i, h in enumerate(hits))
+    return f"{answer}\n\nSOURCES:\n{src}"
+
+
+@mcp.tool()
+def draco_search(query: str, k: int = 8) -> str:
+    """Search DRACO's book library (BM25) for passages matching a topic."""
+    hits = core.search_library(query, k=max(1, min(k, 25)))
+    if not hits:
+        return "No passages matched."
+    return "\n\n".join(
+        f"[{h['title']} · {h['category']} · score {h['score']}]\n{h['text'][:600]}"
+        for h in hits)
+
+
+@mcp.tool()
+def draco_status() -> str:
+    """DRACO health + library size."""
+    s = core.get_stats()
+    return (f"DRACO {CFG['model']} · {s['books']} books · {s['chunks']} passages · "
+            f"API: {CFG['base_url']}/api · MCP: {CFG['base_url']}/mcp")
+
+
+app = mcp.streamable_http_app()
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="127.0.0.1", port=MCP_PORT, log_level="warning")