simplecode-mcp 0.1.0
Turn internal code libraries into AI-accessible knowledge sources via MCP server with semantic search
License: MIT
Requires Python: >=3.11
Keywords: ai, code-analysis, code-indexing, documentation, embeddings, llm, mcp, semantic-search

# SimpleCodeMCP

**Turn your internal code libraries into AI-accessible knowledge sources.**

SimpleCodeMCP is an open-source tool that indexes internal code libraries and exposes them through a Model Context Protocol (MCP) server. This enables AI coding agents (Claude, GitHub Copilot, etc.) to provide precise, context-aware assistance for company-internal libraries—even when documentation is sparse or outdated.

## The Problem

In many organizations:
- **Team A** builds internal libraries (e.g., `pythonpackage1`)
- **Team B** uses these libraries to implement new software
- Documentation is often incomplete, outdated, or missing
- This leads to frequent misuse, implementation errors, and repeated questions

## The Solution

SimpleCodeMCP scans and indexes your internal library's:
- Public and internal APIs
- Function/class signatures and type hints
- Docstrings and comments
- Tests (as usage examples)
- Code structure and relationships

It then exposes this knowledge through an MCP server that AI agents can query to:
- List available functions and classes
- Inspect signatures and behavior
- Retrieve real usage examples from tests
- Search relevant parts of the codebase semantically

## Architecture

```
┌─────────────────────────────────────────────────────────┐
│  Your Library Repository                                │
│  ├── src/           (source code)                       │
│  ├── tests/         (usage examples)                    │
│  └── examples/      (additional examples)               │
└────────────────────┬────────────────────────────────────┘
                     │
                     ▼
          ┌──────────────────────┐
          │  Indexer Component   │
          │  ─────────────────   │
          │  • AST Parser        │  Extract structure
          │  • Docstring Parser  │  Extract documentation
          │  • Test Parser       │  Find usage patterns
          │  • Static Analyzer   │  Infer types & relationships
          └──────────┬───────────┘
                     │
                     ▼
          ┌──────────────────────┐
          │  Storage Layer       │
          │  ─────────────────   │
          │  • ChromaDB          │  Semantic search (embeddings)
          │  • Metadata Store    │  Signatures, paths, etc.
          └──────────┬───────────┘
                     │
                     ▼
          ┌──────────────────────┐
          │  MCP Server          │
          │  (FastAPI + MCP SDK) │
          │  ─────────────────   │
          │  Available Tools:    │
          │  • list_api          │
          │  • get_signature     │
          │  • search_code       │
          │  • get_examples      │
          │  • get_tests         │
          └──────────┬───────────┘
                     │
                     ▼
          ┌──────────────────────┐
          │  AI Agent (Client)   │
          │  Claude, Copilot,    │
          │  or any MCP client   │
          └──────────────────────┘
```

## Features

### Multi-Language Support
- **Python** (MVP with full AST parsing, type inference)
- **C++** (planned)
- **JavaScript/TypeScript** (planned)
- **Java** (planned)
- **Go** (planned)
- Extensible architecture for additional languages

### Multiple Embedding Providers
- **Local** (sentence-transformers) - Free, offline, privacy-friendly
- **OpenAI** - High quality, fast, cloud-based
- **Azure OpenAI** - Enterprise support, data residency control

See [EMBEDDING_PROVIDERS.md](EMBEDDING_PROVIDERS.md) for detailed comparison and setup.

### MCP Tools

The server exposes the following tools to AI agents:

#### `list_api`
Lists all available functions, classes, and modules in the library.

**Parameters:**
- `module` (optional): Filter by specific module/namespace

**Returns:**
```json
{
  "functions": ["calculate_total", "validate_email"],
  "classes": ["User", "Order"],
  "modules": ["core", "utils", "api"]
}
```

#### `get_signature`
Retrieves detailed signature information for a function or class.

**Parameters:**
- `name`: Function or class name

**Returns:**
```json
{
  "name": "calculate_total",
  "signature": "calculate_total(items: List[Item], tax_rate: float = 0.19) -> Decimal",
  "docstring": "Calculate the total price including tax...",
  "file": "src/billing.py",
  "line": 45,
  "parameters": [
    {"name": "items", "type": "List[Item]", "required": true},
    {"name": "tax_rate", "type": "float", "default": "0.19"}
  ],
  "return_type": "Decimal"
}
```

#### `search_code`
Semantic search across the codebase using natural language.

**Parameters:**
- `query`: Natural language query (e.g., "How do I validate an email?")
- `limit` (optional): Maximum results (default: 10)

**Returns:**
```json
{
  "results": [
    {
      "name": "validate_email",
      "relevance_score": 0.92,
      "signature": "validate_email(email: str) -> bool",
      "docstring": "Validates email format using regex...",
      "file": "src/utils/validation.py"
    }
  ]
}
```

#### `get_examples`
Retrieves usage examples from tests and example files.

**Parameters:**
- `name`: Function or class name

**Returns:**
```json
{
  "examples": [
    {
      "source": "tests/test_billing.py",
      "code": "result = calculate_total(items=[item1, item2], tax_rate=0.19)\nassert result == Decimal('119.00')",
      "description": "Basic usage with two items"
    }
  ]
}
```

#### `get_tests`
Retrieves all tests related to a function or class.

**Parameters:**
- `name`: Function or class name

**Returns:**
```json
{
  "tests": [
    {
      "test_name": "test_calculate_total_with_default_tax",
      "file": "tests/test_billing.py",
      "line": 12,
      "code": "..."
    }
  ]
}
```

## Installation

### For Users

```bash
pip install simplecode-mcp
```

### For Development

This project uses [uv](https://github.com/astral-sh/uv) for fast Python package management:

```bash
# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone the repository
git clone https://github.com/yourusername/simplecode-mcp.git
cd simplecode-mcp

# Install dependencies and create virtual environment
uv sync

# Activate the virtual environment
source .venv/bin/activate  # On Unix/macOS
# or
.venv\Scripts\activate  # On Windows
```

## Quick Start

### 1. Index Your Library

Create a configuration file `simplecode_mcp.yaml`:

```yaml
library:
  name: "my-internal-lib"
  path: "/path/to/my-internal-lib"
  language: "python"  # python, c++, javascript, typescript, java, go
  include_private: true  # Index _internal functions too

indexing:
  trigger: "manual"  # manual | on_commit | watch
  embedding_model: "local"  # local (sentence-transformers) | openai

server:
  host: "localhost"
  port: 8000
  auth: null  # Optional: bearer_token for authentication
```

Index your library:

```bash
simplecode-mcp reindex
```

### 2. Start the MCP Server

```bash
simplecode-mcp serve
```

The server will start on `http://localhost:8000`.

### 3. Connect Your AI Agent

Add the MCP server to your agent's configuration:

**For Claude Desktop (`claude_desktop_config.json`):**
```json
{
  "mcpServers": {
    "my-internal-lib": {
      "command": "simplecode-mcp",
      "args": ["serve"],
      "cwd": "/path/to/my-internal-lib"
    }
  }
}
```

**For GitHub Copilot (`mcp.json`):**
```json
{
  "servers": {
    "my-internal-lib": {
      "url": "http://localhost:8000"
    }
  }
}
```

### 4. Use in Your IDE

Your AI agent can now answer questions like:
- "How do I use the `calculate_total` function?"
- "Show me examples of email validation"
- "What parameters does the `User` class constructor take?"

## Configuration Options

### Indexing Triggers

- **`manual`**: Run `simplecode-mcp reindex` manually
- **`on_commit`**: Automatically reindex on git commits (via git hook)
- **`watch`**: Watch for file changes and reindex automatically

### Embedding Models

- **`local`**: Use sentence-transformers (e.g., `all-MiniLM-L6-v2`)
  - Pros: No external API calls, works offline
  - Cons: Slower, lower quality for complex queries
  
- **`openai`**: Use OpenAI's embedding API
  - Pros: Fast, high quality
  - Cons: Requires API key, not fully offline

### Authentication

For internal company use, you can enable bearer token authentication:

```yaml
server:
  auth:
    type: "bearer"
    token: "your-secret-token"
```

## Use Cases

### 1. Library Owner Perspective
You maintain an internal Python package used by 10 teams. Instead of answering the same questions repeatedly:

1. Run SimpleCodeMCP once on your library
2. Share the MCP server endpoint with consumer teams
3. Their AI agents can now answer questions about your library autonomously

### 2. Library Consumer Perspective
You're implementing a new feature using an unfamiliar internal library:

1. Connect your AI agent to the library's MCP server
2. Ask: "How do I authenticate with the internal API?"
3. Get instant, accurate examples from the library's tests

### 3. Onboarding New Developers
New team members can explore internal libraries through their AI assistant without digging through outdated wikis or bothering senior developers.

## Roadmap

### MVP (v0.1)
- [x] Python support (AST parsing, docstrings, tests)
- [x] Manual indexing trigger
- [x] Local embedding model (sentence-transformers)
- [x] Basic MCP tools (list_api, get_signature, search_code, get_examples)
- [x] YAML configuration

### Future Versions
- [ ] Incremental indexing (only changed files)
- [ ] C++ Support
- [ ] JavaScript/TypeScript support
- [ ] Git hook for automatic reindexing
- [ ] File watcher mode
- [ ] OpenAI embedding support
- [ ] Advanced relevance scoring
- [ ] Multi-version support (index v1.x and v2.x simultaneously)
- [ ] Web UI for browsing indexed libraries
- [ ] Integration with internal documentation systems

## Contributing

Contributions are welcome! This project uses [uv](https://github.com/astral-sh/uv) for dependency management.

### Setup Development Environment

```bash
# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone and setup
git clone https://github.com/yourusername/simplecode-mcp.git
cd simplecode-mcp
uv sync

# Run tests
uv run pytest

# Run the CLI in development mode
uv run simplecode-mcp --help
```

### Areas We'd Love Help With
- Support for additional languages (JS/TS, Java, Go, Rust)
- Better test example extraction
- Performance optimizations for large codebases
- Alternative embedding models

## License

MIT License - see [LICENSE](LICENSE) for details.

## Why "SimpleCodeMCP"?

Because complex internal libraries deserve **simple**, accessible knowledge interfaces. No more outdated docs, no more digging through source code—just ask your AI agent.

---

**Built for teams that move fast and break things (but want to break fewer things).**