Sustainable LibreChat setup: official manifest, config layer, brand overlay
- CloudronManifest.json: byte-identical copy of the OFFICIAL appstore manifest (the hand-edited one stripped addons and crash-looped the app on 2026-10-18) - Dockerfile: pinned base (202610150224320000), brand assets only + title sed - librechat.yaml: schema 1.3.17; chat models only in picker (qwen-image moved to the agent image tool); speech via gateway with capped key refs; SearXNG web search; inference-docs MCP server; forward-compatible interface.theme - mcp/server.py: docs MCP server (stdio, stdlib-only, tool search_docs) - assets/: co-op logo + favicons/PWA icons
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#!/usr/bin/env python3
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"""Inference Cooperative docs MCP server (stdio, stdlib-only).
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Exposes a `search_docs` tool over the co-op documentation (co-op/docs on
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git.inference.coop). Members opt in per conversation via the tools palette.
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Design notes:
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- stdio transport: newline-delimited JSON-RPC 2.0 on stdin/stdout (the MCP
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stdio protocol). All logging goes to stderr — stdout is protocol-only.
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- No third-party dependencies: the corpus is fetched from the public Gitea
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raw API, chunked by heading, and ranked with a compact BM25.
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- The index refreshes lazily if older than DOCS_TTL seconds (default 30 min).
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"""
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import json
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import math
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import re
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import sys
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import time
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import urllib.request
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from concurrent.futures import ThreadPoolExecutor
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DOCS_REPO = "co-op/docs"
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GITEA_API = "https://git.inference.coop/api/v1"
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DOCS_TTL = 30 * 60
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MAX_CHUNKS_RETURNED = 5
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SERVER_NAME = "inference-cooperative-docs"
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SERVER_VERSION = "1.0.0"
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_index = {"chunks": [], "built_at": 0.0}
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_lock_held = False
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TOKEN_RE = re.compile(r"[a-z0-9]+")
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def log(msg):
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sys.stderr.write(f"[inference-docs-mcp] {msg}\n")
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sys.stderr.flush()
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def fetch_json(url):
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with urllib.request.urlopen(url, timeout=30) as r:
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return json.loads(r.read().decode("utf-8"))
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def fetch_text(url):
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with urllib.request.urlopen(url, timeout=30) as r:
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return r.read().decode("utf-8")
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def list_doc_files():
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files = fetch_json(f"{GITEA_API}/repos/{DOCS_REPO}/contents")
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return [
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{"path": f["path"], "name": f["name"]}
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for f in files
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if f["type"] == "file" and f["name"].endswith(".md")
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]
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def chunk_markdown(path, text):
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"""Split a doc into section chunks keyed by its top title + heading."""
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lines = text.splitlines()
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title = lines[0].lstrip("# ").strip() if lines and lines[0].startswith("#") else path
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sections = []
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current_head = "Overview"
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current = []
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def flush():
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body = "\n".join(current).strip()
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if len(body) < 40:
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return
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sections.append({"doc": title, "path": path, "section": current_head, "text": body})
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for line in lines:
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if line.startswith("## "):
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flush()
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current_head = line.lstrip("# ").strip()
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current = []
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else:
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current.append(line)
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flush()
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return sections
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def tokenize(text):
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return TOKEN_RE.findall(text.lower())
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def build_index():
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chunks = []
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docs = list_doc_files()
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def load(doc):
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try:
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raw = fetch_text(f"{GITEA_API}/repos/{DOCS_REPO}/raw/{doc['path']}")
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return chunk_markdown(doc["path"], raw)
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except Exception as exc: # noqa: BLE001 - one bad doc shouldn't kill the index
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log(f"skipping {doc['path']}: {exc}")
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return []
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with ThreadPoolExecutor(max_workers=6) as pool:
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for parts in pool.map(load, docs):
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chunks.extend(parts)
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# BM25 statistics
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doc_terms = [tokenize(c["text"]) for c in chunks]
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n = len(chunks)
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avg_len = sum(len(t) for t in doc_terms) / max(n, 1)
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df = {}
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for terms in doc_terms:
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for term in set(terms):
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df[term] = df.get(term, 0) + 1
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for c, terms in zip(chunks, doc_terms):
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c["_tf"] = {}
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for t in terms:
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c["_tf"][t] = c["_tf"].get(t, 0) + 1
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c["_len"] = len(terms)
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_index["chunks"] = chunks
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_index["df"] = df
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_index["n"] = n
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_index["avg_len"] = avg_len
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_index["built_at"] = time.time()
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log(f"index built: {n} chunks from {len(docs)} docs")
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def ensure_index():
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if not _index["chunks"] or time.time() - _index["built_at"] > DOCS_TTL:
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build_index()
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def search(query, limit=MAX_CHUNKS_RETURNED):
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ensure_index()
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k1, b = 1.5, 0.75
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q_terms = tokenize(query)
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scored = []
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for c in _index["chunks"]:
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score = 0.0
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for t in q_terms:
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tf = c["_tf"].get(t, 0)
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if not tf:
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continue
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idf = math.log(1 + (_index["n"] - _index["df"].get(t, 0) + 0.5) / (_index["df"].get(t, 0) + 0.5))
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score += idf * tf * (k1 + 1) / (tf + k1 * (1 - b + b * c["_len"] / _index["avg_len"]))
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if score > 0:
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scored.append((score, c))
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scored.sort(key=lambda pair: -pair[0])
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return scored[:limit]
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def tool_search_docs(args):
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query = str(args.get("query") or "").strip()
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if not query:
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return {"content": [{"type": "text", "text": "Provide a query."}]}
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limit = min(int(args.get("limit") or MAX_CHUNKS_RETURNED), 10)
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results = search(query, limit)
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if not results:
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return {"content": [{"type": "text", "text": "No matching documentation found."}]}
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parts = []
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for score, c in results:
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parts.append(f"[{c['doc']} — {c['section']}] (relevance {score:.2f})\n{c['text']}")
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return {"content": [{"type": "text", "text": "\n\n---\n\n".join(parts)}]}
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TOOLS = [
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{
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"name": "search_docs",
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"description": (
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"Search the Inference Cooperative documentation (getting started, models, "
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"API access, charter, policies). Returns the most relevant doc sections."
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),
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"inputSchema": {
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"type": "object",
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"properties": {
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"query": {"type": "string", "description": "What to look for in the docs"},
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"limit": {"type": "integer", "description": "Max sections to return (default 5)"},
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},
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"required": ["query"],
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},
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}
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]
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TOOLS_HANDLERS = {"search_docs": tool_search_docs}
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def handle(msg):
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method = msg.get("method")
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msg_id = msg.get("id")
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if method == "initialize":
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return {
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"jsonrpc": "2.0",
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"id": msg_id,
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"result": {
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"protocolVersion": "2024-11-05",
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"capabilities": {"tools": {}},
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"serverInfo": {"name": SERVER_NAME, "version": SERVER_VERSION},
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},
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}
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if method == "ping":
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return {"jsonrpc": "2.0", "id": msg_id, "result": {}}
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if method == "tools/list":
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return {"jsonrpc": "2.0", "id": msg_id, "result": {"tools": TOOLS}}
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if method == "tools/call":
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params = msg.get("params") or {}
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name = params.get("name")
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handler = TOOLS_HANDLERS.get(name)
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if not handler:
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return {
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"jsonrpc": "2.0",
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"id": msg_id,
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"error": {"code": -32602, "message": f"Unknown tool: {name}"},
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}
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try:
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result = handler(params.get("arguments") or {})
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return {"jsonrpc": "2.0", "id": msg_id, "result": result}
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except Exception as exc: # noqa: BLE001 - report tool errors in-band
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log(f"tool error: {exc}")
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return {
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"jsonrpc": "2.0",
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"id": msg_id,
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"result": {"content": [{"type": "text", "text": f"Search failed: {exc}"}], "isError": True},
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}
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if method and method.startswith("notifications/"):
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return None
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if msg_id is not None:
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return {"jsonrpc": "2.0", "id": msg_id, "error": {"code": -32601, "message": "Method not found"}}
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return None
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def main():
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log("starting (stdio)")
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for line in sys.stdin:
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line = line.strip()
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if not line:
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continue
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try:
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msg = json.loads(line)
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except json.JSONDecodeError as exc:
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log(f"bad json: {exc}")
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continue
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try:
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resp = handle(msg)
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except Exception as exc: # noqa: BLE001 - never crash the protocol loop
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log(f"handler crash: {exc}")
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if msg.get("id") is not None:
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resp = {"jsonrpc": "2.0", "id": msg["id"], "error": {"code": -32603, "message": "Internal error"}}
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else:
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resp = None
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if resp is not None:
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sys.stdout.write(json.dumps(resp) + "\n")
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sys.stdout.flush()
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if __name__ == "__main__":
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main()
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