How do I install MCP Codebase Index?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does MCP Codebase Index need an API key?

No required environment variable is listed on this record.

What transport does MCP Codebase Index use?

MCP Codebase Index runs locally over stdio.

When was MCP Codebase Index last checked?

Last checked 2026-11-09. Structural codebase indexer with 17 query tools. 87% token reduction. Zero dependencies.

MCP Codebase Index MCP server

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

Language	Method	Extracts
Python (`.py`)	AST parsing	Functions, classes, methods, imports, dependency graph
TypeScript/JS (`.ts`, `.tsx`, `.js`, `.jsx`)	Regex-based	Functions, arrow functions, classes, interfaces, type aliases, imports
Go (`.go`)	Regex-based	Functions, methods (receiver-based), structs, interfaces, type aliases, imports, doc comments
Rust (`.rs`)	Regex-based	Functions (`pub`/`async`/`const`/`unsafe`), structs, enums, traits, impl blocks, use statements, attributes, doc comments, macro_rules
C# (`.cs`)	Regex-based	Classes, interfaces, structs, enums, records, methods, constructors, using directives, `[Attributes]`, `///` XML doc comments
Markdown/Text (`.md`, `.txt`, `.rst`)	Heading detection	Sections (# headings, underlines, numbered, ALL-CAPS)
Other	Generic	Line counts only

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:

Cache hit (exact match): If the cached git ref matches the current HEAD, the index loads instantly from disk — no parsing, no file walking.
Cache hit (small changeset): If ≤20 files changed since the cached ref, the cached index is loaded and incrementally updated on the first query.
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)

Tool	Description
`get_project_summary`	File count, packages, top classes/functions
`list_files`	List indexed files with optional glob filter
`get_structure_summary`	Structure of a file or the whole project
`get_functions`	List functions with name, lines, params
`get_classes`	List classes with name, lines, methods, bases
`get_imports`	List imports with module, names, line
`get_function_source`	Full source of a function/method
`get_class_source`	Full source of a class
`find_symbol`	Find where a symbol is defined (file, line, type)
`get_dependencies`	What a symbol calls/uses
`get_dependents`	What calls/uses a symbol
`get_change_impact`	Direct + transitive dependents
`get_call_chain`	Shortest dependency path (BFS)
`get_file_dependencies`	Files imported by a given file
`get_file_dependents`	Files that import from a given file
`search_codebase`	Regex search across all files (max 100 results)
`reindex`	Force full re-index (rarely needed — incremental updates happen automatically in git repos)
`get_usage_stats`	Session efficiency stats: tool calls, characters returned vs total source, estimated token savings

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

Project	Files	Lines	Functions	Classes	Index Time	Peak Memory
RMLPlus	36	7,762	237	55	0.9s	2.4 MB
FastAPI	2,556	332,160	4,139	617	5.7s	55 MB
Django	3,714	707,493	29,995	7,371	36.2s	126 MB
CPython	2,464	1,115,334	59,620	9,037	55.9s	197 MB

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:

Query	Response	Total Source	Reduction
`find_symbol("TestCase")`	67 chars	41,077,561 chars	99.9998%
`get_dependencies("compile")`	115 chars	41,077,561 chars	99.9997%
`get_change_impact("TestCase")`	16,812 chars	41,077,561 chars	99.96%
`get_function_source("compile")`	4,531 chars	41,077,561 chars	99.99%
`get_function_source("run_unittest")`	439 chars	41,077,561 chars	99.999%

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:

Query	RMLPlus	FastAPI	Django	CPython
`find_symbol`	0.01ms	0.01ms	0.03ms	0.08ms
`get_dependencies`	0.00ms	0.00ms	0.00ms	0.01ms
`get_change_impact`	0.02ms	0.00ms	2.81ms	0.45ms
`get_function_source`	0.01ms	0.02ms	0.03ms	0.10ms

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.

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