codetex-mcp

A commit-aware code context manager for LLMs. Indexes Git repositories into a multi-tier knowledge hierarchy — repo overviews, file summaries, and symbol details — stored in SQLite with vector search. Serves context to LLM clients via the Model Context Protocol (MCP) or a local CLI.

What It Does

codetex builds a structured, searchable index of your codebase that LLMs can query on demand:

• Tier 1 — Repo Overview: Purpose, architecture, directory structure, key technologies, entry points
• Tier 2 — File Summaries: Per-file purpose, public interfaces, dependencies, roles
• Tier 3 — Symbol Details: Function/class signatures, parameters, return types, call relationships

Summaries are generated by an LLM (Anthropic Claude). Embeddings are computed locally with sentence-transformers for semantic search. Everything is stored in a single SQLite database with sqlite-vec for vector queries.

Incremental sync means only changed files are re-analyzed when you update your code.

Requirements

• Python 3.12+
• Git
• An Anthropic API key (for indexing)

Installation

# With pip
pip install codetex-mcp

# With uv (recommended)
uv tool install codetex-mcp

Quick Start

1. Set your Anthropic API key

# Via environment variable
export ANTHROPIC_API_KEY=sk-ant-...

# Or via config
codetex config set llm.api_key sk-ant-...

2. Add a repository

# Local repo
codetex add /path/to/your/project

# Remote repo (clones to ~/.codetex/repos/)
codetex add https://github.com/user/repo.git

3. Index it

# Preview what indexing will cost (no API calls)
codetex index my-project --dry-run

# Build the full index
codetex index my-project

4. Query your codebase

# Repo overview (Tier 1)
codetex context my-project

# File summary (Tier 2)
codetex context my-project --file src/auth/login.py

# Symbol detail (Tier 3)
codetex context my-project --symbol authenticate_user

# Semantic search
codetex context my-project --query "how is authentication implemented?"

5. Keep it up to date

# Incremental sync — only re-analyzes changed files
codetex sync my-project

MCP Server Setup

The MCP server lets LLM clients (like Claude Code, Cursor, Windsurf, etc.) query your indexed codebases directly.

Claude Code

Add to your Claude Code MCP settings (~/.claude/claude_desktop_config.json):

{
  "mcpServers": {
    "codetex": {
      "command": "codetex",
      "args": ["serve"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

If you installed with uv tool, use the full path:

{
  "mcpServers": {
    "codetex": {
      "command": "/path/to/codetex",
      "args": ["serve"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

Find the path with which codetex or uv tool dir.

Other MCP Clients

Any client that supports MCP stdio transport can use codetex. The server command is:

codetex serve

Available MCP Tools

Once connected, the LLM has access to 7 tools:

CLI Reference

codetex add <target>

Register a git repository. Accepts a local path or remote URL.

codetex add .                                    # Current directory
codetex add /path/to/repo                        # Local path
codetex add https://github.com/user/repo.git     # Remote (clones locally)
codetex add [email protected]:user/repo.git         # SSH remote

codetex index <repo-name>

Build a full index for a registered repository.

codetex index my-project                # Full index
codetex index my-project --dry-run      # Preview (files, symbols, estimated LLM calls/tokens)
codetex index my-project --path src/    # Index only files under src/

codetex sync <repo-name>

Incremental sync to the current HEAD. Only files changed since the last indexed commit are re-analyzed.

codetex sync my-project                 # Sync changes
codetex sync my-project --dry-run       # Preview what would change
codetex sync my-project --path src/     # Sync only changes under src/

codetex context <repo-name>

Query indexed context at any tier.

codetex context my-project                              # Tier 1: repo overview
codetex context my-project --file src/main.py           # Tier 2: file summary
codetex context my-project --symbol MyClass             # Tier 3: symbol detail
codetex context my-project --query "error handling"     # Semantic search

codetex status <repo-name>

Show index status: indexed commit, current HEAD, staleness, file/symbol counts, token usage.

codetex list

List all registered repositories with their index status.

codetex config show

Display the current configuration.

codetex config set <key> <value>

Update a configuration value.

codetex config set llm.api_key sk-ant-...
codetex config set llm.model claude-sonnet-4-5-20250929
codetex config set indexing.max_file_size_kb 1024
codetex config set indexing.max_concurrent_llm_calls 10

Configuration

Configuration is loaded in layers (last wins):

1. Defaults — sensible out-of-the-box values
2. TOML file — ~/.codetex/config.toml
3. Environment variables — override everything

Config file

# ~/.codetex/config.toml

[storage]
data_dir = "~/.codetex"                  # Base directory for DB and cloned repos

[llm]
provider = "anthropic"                   # LLM provider (currently: anthropic)
model = "claude-sonnet-4-5-20250929"     # Model used for summarization
api_key = "sk-ant-..."                   # Anthropic API key

[indexing]
max_file_size_kb = 512                   # Skip files larger than this
max_concurrent_llm_calls = 5             # Parallel LLM requests during indexing
tier1_rebuild_threshold = 0.10           # Rebuild repo overview if >=10% of files changed on sync

[embedding]
model = "all-MiniLM-L6-v2"              # Sentence-transformers model for embeddings

Environment variables

File Exclusion

Files are filtered through multiple stages:

1. Default excludes — node_modules/, __pycache__/, .git/, dist/, build/, .venv/, *.lock, *.min.js, *.pyc, *.so, etc.
2. `.gitignore` — standard gitignore rules from your repo
3. `.codetexignore` — same syntax as .gitignore, placed in your repo root. Use !pattern to un-ignore files
4. File size — files exceeding max_file_size_kb are skipped
5. Binary detection — files with null bytes in the first 8 KB are skipped

Language Support

Tree-sitter grammars for all 8 languages are installed automatically. For other languages, the fallback parser uses regex patterns to extract functions, classes, and imports.

Architecture

CLI (Typer) ──┐
              ├──▶ Core Services (Indexer, Syncer, ContextStore, SearchEngine)
MCP (FastMCP)─┘         │              │              │
                    Analysis        LLM Provider    Embeddings
                 (tree-sitter +    (Anthropic)    (sentence-transformers)
                  regex fallback)       │              │
                         └──────────────┴──────────────┘
                                        │
                                   SQLite + sqlite-vec

• Two entry points (CLI and MCP server) share the same core service layer
• No DI framework — services are wired via a create_app() factory
• All core services are async — CLI bridges with asyncio.run()
• Embeddings are local — no external API calls for vector search (model auto-downloads on first run, ~90 MB)
• Single SQLite database — 6 main tables + 2 vector tables (384-dimensional embeddings)

Development

git clone https://github.com/mrosata/codetex-mcp.git
cd codetex-mcp

# Install dependencies (including dev)
uv sync

# Run tests
uv run pytest

# Run tests with coverage
uv run pytest --cov=codetex_mcp

# Lint and format
uv run ruff check src/ tests/
uv run ruff format src/ tests/

# Type check
uv run mypy src/

Releasing

Releases are automated via GitHub Actions and python-semantic-release. Version bumps are driven by conventional commit messages on main.

Commit message format

A BREAKING CHANGE: line in the commit body also triggers a major bump.

How it works

1. Push or merge a PR to main
2. CI runs lint, type check, and tests
3. The release workflow analyzes commits since the last tag
4. If a version bump is needed, it:

• Updates the version in pyproject.toml
• Creates a git tag (e.g., v0.2.0)
• Publishes a GitHub Release with a changelog
• Builds and publishes the package to PyPI

Manual release (not recommended)

If you need to release without the automation:

uv build
uv publish

License

MIT