mcp-codebase-index

A structural codebase indexer with an MCP server for AI-assisted development. Zero runtime dependencies — uses Python's `ast` module for Python analysis and regex-based parsing for TypeScript/JS, Go, Rust, and C#. Requires Python 3.11+.

What It Does

Indexes codebases by parsing source files into structural metadata -- functions, classes, imports, dependency graphs, and cross-file call chains -- then exposes 18 query tools via the Model Context Protocol, enabling Claude Code and other MCP clients to navigate codebases efficiently without reading entire files.

Automatic incremental re-indexing: In git repositories, the index stays up to date automatically. Before every query, the server checks `git diff` and `git status` (~1-2ms). If files changed, only those files are re-parsed and the dependency graph is rebuilt. No need to manually call `reindex` after edits, branch switches, or pulls.

Persistent disk cache: The index is saved to a pickle cache file (`.codebase-index-cache.pkl`) after every build. On subsequent server starts, the cache is loaded and validated against the current git HEAD — if the ref matches, startup is instant. If a small number of files changed (≤20), the cached index is loaded and incrementally updated instead of rebuilt from scratch. This eliminates the cold-start penalty when restarting Claude Code sessions, restarting the MCP server, or resuming work after context compaction.

Language Support

Installation

pip install "mcp-codebase-index[mcp]"

The `[mcp]` extra includes the MCP server dependency. Omit it if you only need the programmatic API.

For development (from a local clone):

pip install -e ".[dev,mcp]"

MCP Server

Running

# As a console script
PROJECT_ROOT=/path/to/project mcp-codebase-index

# As a Python module
PROJECT_ROOT=/path/to/project python -m mcp_codebase_index.server

`PROJECT_ROOT` specifies which directory to index. Defaults to the current working directory.

Persistent Cache

In git repositories, the server automatically caches the index to `.codebase-index-cache.pkl` in the project root. On startup:

1. Cache hit (exact match): If the cached git ref matches the current HEAD, the index loads instantly from disk — no parsing, no file walking.

2. Cache hit (small changeset): If ≤20 files changed since the cached ref, the cached index is loaded and incrementally updated on the first query.

3. Cache miss: If the changeset is large or no cache exists, a full rebuild runs and saves a new cache.

Add `.codebase-index-cache.pkl` to your `.gitignore` — it's a local-only build artifact.

Configuring with OpenClaw

Install the package on the machine where OpenClaw is running:

# Local install
pip install "mcp-codebase-index[mcp]"

# Or inside a Docker container / remote VPS
docker exec -it openclaw bash
pip install "mcp-codebase-index[mcp]"

Add the MCP server to your OpenClaw agent config (`openclaw.json`):

{
  "agents": {
    "list": [{
      "id": "main",
      "mcp": {
        "servers": [
          {
            "name": "codebase-index",
            "command": "mcp-codebase-index",
            "env": {
              "PROJECT_ROOT": "/path/to/project"
            }
          }
        ]
      }
    }]
  }
}

Restart OpenClaw and verify the connection:

openclaw mcp list

All 18 tools will be available to your agent.

Performance note: The server automatically detects file changes via `git diff` before every query (~1-2ms) and incrementally re-indexes only what changed. However, OpenClaw's default MCP integration via mcporter spawns a fresh server process per tool call, which discards the in-memory index and forces a full rebuild each time (~1-2s for small projects, longer for large ones). With persistent caching, these cold starts are now significantly faster — the server loads from the disk cache instead of re-parsing the entire codebase. For persistent connections (avoiding even the cache load overhead), use the openclaw-mcp-adapter plugin, which connects once at startup and keeps the server running:

pip install openclaw-mcp-adapter

Configuring with Claude Code

Add to your project's `.mcp.json`:

{
  "mcpServers": {
    "codebase-index": {
      "command": "mcp-codebase-index",
      "env": {
        "PROJECT_ROOT": "/path/to/project"
      }
    }
  }
}

Or using the Python module directly (useful if installed in a virtualenv):

{
  "mcpServers": {
    "codebase-index": {
      "command": "/path/to/.venv/bin/python3",
      "args": ["-m", "mcp_codebase_index.server"],
      "env": {
        "PROJECT_ROOT": "/path/to/project"
      }
    }
  }
}

Reinforcing Tool Usage with Hooks

Claude Code tends to default to built-in Glob/Grep/Read tools even when codebase-index is available. In addition to CLAUDE.md instructions (see below), you can add hooks that fire on every prompt to reinforce the behavior. Add this to `.claude/settings.local.json`:

{
  "hooks": {
    "SessionStart": [
      {
        "hooks": [
          {
            "type": "command",
            "command": "echo 'CRITICAL REMINDER: Use codebase-index MCP tools FIRST for ALL code navigation (find_symbol, get_function_source, search_codebase, get_dependencies, etc). Only fall back to Glob/Grep/Read for non-code files.'"
          }
        ]
      }
    ],
    "UserPromptSubmit": [
      {
        "hooks": [
          {
            "type": "command",
            "command": "echo 'Use codebase-index MCP tools first for code navigation.'"
          }
        ]
      }
    ]
  }
}

Hook stdout is injected as context Claude sees before responding. `SessionStart` fires on startup, resume, and context compaction. `UserPromptSubmit` fires on every turn.

Important: Make the AI Actually Use Indexed Tools

By default, AI assistants will ignore the indexed tools and fall back to reading entire files with Glob/Grep/Read. Soft language like "prefer" gets rationalized away. Add this to your project's `CLAUDE.md` (or equivalent instructions file) with mandatory language:

## Codebase Navigation — MANDATORY

You MUST use codebase-index MCP tools FIRST when exploring or navigating the codebase. This is not optional.

- ALWAYS start with: get_project_summary, find_symbol, get_function_source, get_class_source,
  get_structure_summary, get_dependencies, get_dependents, get_change_impact, get_call_chain, search_codebase
- Only fall back to Read/Glob/Grep when codebase-index tools genuinely don't have what you need
  (e.g. reading non-code files, config, frontmatter)
- If you catch yourself reaching for Glob/Grep/Read to find or understand code, STOP and use
  codebase-index instead

The word "prefer" is too weak — models treat it as a suggestion and default to familiar tools. Mandatory language with explicit fallback criteria is what actually changes behavior.

Available Tools (18)

Benchmarks

Tested across four real-world projects on an M-series MacBook Pro, from a small project to CPython itself (1.1 million lines):

Index Build Performance

With persistent caching, subsequent startups bypass the full build entirely. Cache load time is negligible compared to parsing — a cache hit on CPython restores the full index in under a second instead of 56s.

Query Response Size vs Total Source

Querying CPython — 41 million characters of source code:

`find_symbol` returns 54-67 characters regardless of whether the project is 7K lines or 1.1M lines. Response size scales with the answer, not the codebase.

`get_change_impact("TestCase")` on CPython found 154 direct dependents and 492 transitive dependents in 0.45ms — the kind of query that's impossible without a dependency graph. Use `max_direct` and `max_transitive` to cap output to your token budget.

Query Response Time

All targeted queries return in sub-millisecond time, even on CPython's 1.1M lines:

Run the benchmarks yourself: `python benchmarks/benchmark.py`

How Is This Different from LSP?

LSP answers "where is this function?" — mcp-codebase-index answers "what happens if I change it?" LSP is point queries: one symbol, one file, one position. It can tell you where `LLMClient` is defined and who references it. But ask "what breaks transitively if I refactor `LLMClient`?" and LSP has nothing. This tool returns 11 direct dependents and 31 transitive impacts in a single call — 204 characters. To get the same answer from LSP, the AI would need to chain dozens of find-reference calls recursively, reading files at every step, burning thousands of tokens to reconstruct what the dependency graph already knows.

LSP also requires you to install a separate language server for every language in your project — pyright for Python, vtsls for TypeScript, gopls for Go. Each one is a heavyweight binary with its own dependencies and configuration. mcp-codebase-index is zero dependencies, handles Python + TypeScript/JS + Go + Rust + C# + Markdown out of the box, and every response has built-in token budget controls (`max_results`, `max_lines`). LSP was built for IDEs. This was built for AI.

Programmatic Usage

from mcp_codebase_index.project_indexer import ProjectIndexer
from mcp_codebase_index.query_api import create_project_query_functions

indexer = ProjectIndexer("/path/to/project", include_patterns=["**/*.py"])
index = indexer.index()
query_funcs = create_project_query_functions(index)

# Use query functions
print(query_funcs["get_project_summary"]())
print(query_funcs["find_symbol"]("MyClass"))
print(query_funcs["get_change_impact"]("some_function"))

Development

pip install -e ".[dev,mcp]"
pytest tests/ -v
ruff check src/ tests/

References

The structural indexer was originally developed as part of the RMLPlus project, an implementation of the Recursive Language Models framework.

License

This project is dual-licensed:

• AGPL-3.0 for open-source use — see LICENSE
• Commercial License for proprietary use — see COMMERCIAL-LICENSE.md

If you're using mcp-codebase-index as a standalone MCP server for development, the AGPL-3.0 license applies at no cost. If you're embedding it in a proprietary product or offering it as part of a hosted service, you'll need a commercial license. See COMMERCIAL-LICENSE.md for details.