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docs-rag-mcp

docs-rag-mcp

by shrish346

docs-rag-mcp

MCP server that searches a local markdown corpus with TF-IDF retrieval and serves full documents as resources.

Architecture: docs/*.md → chunk + TF-IDF index (rag.py) → FastMCP exposes search_docs tool and doc://{path} resource (server.py).

MCP surface

Kind

Name

Description

Tool

search_docs

query + k — returns ranked chunks with path, score, excerpt

Resource

doc://{path}

Full markdown for a relative path (e.g. doc://rag-patterns.md)

Related MCP server: Inkdex

Run

uv sync
uv run fastmcp run server.py                    # stdio (Claude Desktop, Cursor)
uv run fastmcp run server.py -t http -p 8000    # HTTP for MCP Inspector

Test

uv run fastmcp list server.py
uv run fastmcp call server.py search_docs --query "TF-IDF" --k 3

Add your own notes under docs/ and restart the server (or re-run) to re-index.

Claude Desktop

See docs/claude-desktop-config.md for claude_desktop_config.json snippet. Use an absolute --directory path.

Searching Completed

Available Tools

1 tool
search_docsA

Search the documentation corpus with TF-IDF retrieval.

Inputs: query (required), k. Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A4/5.0

    Scored across 1 tool

    Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion between overlapping tools. The purpose of search_docs is singular and clear.

    Naming Consistency5/5

    The single tool name 'search_docs' follows a clear verb_noun pattern, which is consistent for the server's scope. There are no other names to compare, so it is perfectly consistent.

    Tool Count4/5

    A single tool for a docs RAG server is borderline but acceptable, as search is the primary function. However, it feels slightly thin, and additional tools like list_docs or get_doc could enhance usability.

    Completeness4/5

    For a documentation search server, search_docs covers the core retrieval need. Minor gaps exist such as no ability to list available document sources or fetch a specific document directly, but these are not critical for basic RAG workflows.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

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