Codemunch Pro MCP server

CodeMunch Pro

<!-- mcp-name: io.github.BigJai/codemunch-pro -->

Intelligent code indexing MCP server. 15 tools, 10 languages, tree-sitter AST extraction, hybrid search (FTS5 + vector), call graphs, remote repo indexing, incremental indexing.

Save 99% of tokens — get exact function source via byte-offset seek instead of reading entire files.

Install

pip install codemunch-pro

Quick Start

Claude Desktop / Cline

Add to your MCP client config:

{
  "mcpServers": {
    "codemunch-pro": {
      "command": "codemunch-pro"
    }
  }
}

HTTP Server

codemunch-pro --transport streamable-http --port 5002

15 MCP Tools

10 Languages

Python, JavaScript, TypeScript, Go, Rust, Java, C, C++, C#, Ruby

All via tree-sitter-language-pack — zero compilation, pre-built binaries.

Key Features

O(1) Symbol Retrieval

Every symbol stores its byte offset and length. getsymbol seeks directly to the function source — no reading entire files. A 200-byte function from a 40KB file = 99.5% token savings**.

Incremental Indexing

Files are hashed (SHA-256). Only changed files are re-parsed. Re-indexing a 10K file repo after changing one file takes milliseconds.

Hybrid Search (FTS5 + Vector)

Combines BM25 keyword matching with semantic vector similarity using Reciprocal Rank Fusion. Search "authentication middleware" and find authmiddleware, verifytoken, and login_handler.

Call Graphs

Traces function calls through the AST. getcallees("main") shows what main calls. getcallers("authenticate") shows who calls authenticate. Supports depth traversal.

Remote Repo Indexing (v1.1)

Index any public GitHub or GitLab repo by URL — no git binary needed. Downloads the tarball via API, extracts, and indexes. Cached locally with SHA-based freshness checks. Supports private repos with auth tokens and sparse paths.

Full-Text Content Search

Search raw file contents — string literals, TODO comments, config values, error messages. Not just symbol names.

How It Works

1. Parse — tree-sitter builds an AST for each source file
2. Extract — Walk AST to find functions, classes, methods, types, interfaces
3. Store — SQLite database per repo with FTS5 virtual tables
4. Embed — FastEmbed (ONNX, CPU-only) generates 384-dim vectors for semantic search
5. Graph — Call expressions extracted from function bodies, edges stored and resolved
6. Serve — FastMCP exposes 13 tools via stdio or HTTP

Architecture

~/.codemunch-pro/
├── myproject_a1b2c3d4e5f6.db    # Per-repo SQLite database
├── otherproject_7890abcdef.db
└── ...

Each DB contains:
├── files          # Indexed files with SHA-256 hashes
├── symbols        # Functions, classes, methods, types
├── symbols_fts    # FTS5 full-text search index
├── symbols_vec    # sqlite-vec 384-dim vector index
├── call_edges     # Call graph (caller → callee)
└── file_content_fts  # Raw file content search

Use Cases

Shortened. The full README is on GitHub.