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TuringMind MCP Server

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by turingmindai
README.md
# TuringMind MCP Server

[![PyPI version](https://badge.fury.io/py/turingmind-mcp.svg)](https://badge.fury.io/py/turingmind-mcp)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

Model Context Protocol (MCP) server for [TuringMind](https://turingmind.ai) cloud integration. Provides type-safe tools for Claude to authenticate, upload code reviews, fetch repository context, and submit feedback.

> **Requires Python 3.10+** (MCP SDK requirement)

## Why MCP?

Instead of Claude generating raw JSON and curl commands (which can fail silently due to field name mismatches or malformed data), MCP provides:

- **Type-safe tool definitions** — Claude sees the exact schema
- **Validated input** — Errors caught before sending
- **No endpoint guessing** — Correct URLs hardcoded
- **Better error messages** — Clear feedback on failures
- **Simplified login** — Device code flow handled by the server

## Installation

### From PyPI

```bash
pip install turingmind-mcp
```

### With pipx (recommended for CLI tools)

```bash
pipx install turingmind-mcp
```

### From Source

```bash
git clone https://github.com/turingmindai/turingmind-mcp.git
cd turingmind-mcp
pip install -e .
```

### Verify Installation

```bash
turingmind-mcp --help
```

## Quick Start

### Unified Setup (Recommended)

```bash
# Install
pip install turingmind-mcp

# Setup for your platform
turingmind setup claude_desktop  # Claude Desktop
turingmind setup cursor          # Cursor IDE/CLI
turingmind setup claude_cli      # Claude Code CLI

# Diagnose installation
turingmind diagnose
```

### Platform-Specific Setup

TuringMind-MCP supports multiple platforms. Choose your platform:

- **[Claude Desktop](docs/platforms/claude-desktop.md)** - Native MCP integration
- **[Claude Code CLI](docs/platforms/claude-cli.md)** - Command-line with MCP or Skills
- **[Claude SDK](docs/platforms/claude-sdk.md)** - Programmatic Python access
- **[Cursor IDE](docs/platforms/cursor-ide.md)** - Native MCP integration
- **[Cursor CLI](docs/platforms/cursor-cli.md)** - Command-line integration

### Manual Setup (Claude Desktop Example)

1. **Configure Claude Desktop**

Add to your Claude Desktop config file:

| Platform | Path |
|----------|------|
| macOS | `~/Library/Application Support/Claude/claude_desktop_config.json` |
| Windows | `%APPDATA%\Claude\claude_desktop_config.json` |
| Linux | `~/.config/Claude/claude_desktop_config.json` |

```json
{
  "mcpServers": {
    "turingmind": {
      "command": "turingmind-mcp"
    }
  }
}
```

2. **Restart Claude Desktop**

3. **Login to TuringMind**

In Claude, say: "Log me into TuringMind"

Claude will guide you through the device code flow.

## Available Tools

### Authentication

| Tool | Description |
|------|-------------|
| `turingmind_initiate_login` | Start device code auth flow (no API key needed) |
| `turingmind_poll_login` | Complete login and save API key |
| `turingmind_validate_auth` | Check API key and account status |

### Code Review

| Tool | Description |
|------|-------------|
| `turingmind_upload_review` | Upload review results to cloud |
| `turingmind_get_context` | Get memory context for a repository |
| `turingmind_submit_feedback` | Mark issues as fixed, dismissed, or false positive |

## Tool Reference

### `turingmind_initiate_login`

Start device code authentication flow. No API key required.

**Parameters:** None

**Returns:**
- `verification_url` — URL to open in browser
- `user_code` — Code to enter when prompted  
- `device_code` — Use with `turingmind_poll_login`

---

### `turingmind_poll_login`

Poll for authentication completion.

**Parameters:**

| Name | Type | Required | Description |
|------|------|----------|-------------|
| `device_code` | string | ✅ | Device code from `turingmind_initiate_login` |

**Returns:**
- On success: API key (automatically saved to `~/.turingmind/config`)
- On pending: Status message to wait and retry
- On expired: Error message to restart flow

---

### `turingmind_validate_auth`

Validate API key and get account info.

**Parameters:** None

**Returns:**
- Tier (free, pro, team, enterprise)
- Quota remaining
- User ID

---

### `turingmind_upload_review`

Upload code review results to TuringMind cloud.

**Parameters:**

| Name | Type | Required | Description |
|------|------|----------|-------------|
| `repo` | string | ✅ | Repository (owner/repo) |
| `branch` | string | | Git branch name |
| `commit` | string | | Git commit SHA |
| `review_type` | `"quick"` \| `"deep"` | | Review type (default: quick) |
| `issues` | array | | List of issues found |
| `raw_content` | string | | Full review as markdown |
| `summary` | object | | {critical, high, medium, low} counts |
| `files_reviewed` | array | | Files that were reviewed |

**Issue Schema:**

```json
{
  "title": "SQL Injection vulnerability",
  "severity": "critical",
  "category": "security",
  "file": "src/db.py",
  "line": 42,
  "description": "User input passed directly to query",
  "cwe": "CWE-89",
  "confidence": 95
}
```

---

### `turingmind_get_context`

Get memory context for a repository.

**Parameters:**

| Name | Type | Required | Description |
|------|------|----------|-------------|
| `repo` | string | ✅ | Repository (owner/repo) |

**Returns:**
- Recent open issues
- Hotspot files (frequent issues)
- Team conventions
- Known false positive patterns

---

### `turingmind_submit_feedback`

Submit feedback on a code review issue.

**Parameters:**

| Name | Type | Required | Description |
|------|------|----------|-------------|
| `issue_id` | string | ✅ | Issue ID (e.g., `iss_abc123`) |
| `action` | string | ✅ | One of: `fixed`, `dismissed`, `false_positive` |
| `repo` | string | ✅ | Repository (owner/repo) |
| `file` | string | | File path where issue was found |
| `line` | integer | | Line number of the issue |
| `pattern` | string | | For false_positive: pattern to skip in future |
| `reason` | string | | Explanation for the feedback |

**Actions:**
- `fixed` — Issue was resolved (tracks time-to-fix metrics)
- `dismissed` — Issue isn't important (lowers future priority)
- `false_positive` — Not a real issue (adds pattern to memory)

## Usage Examples

### Login Flow

```
User: Log me into TuringMind

Claude: I'll start the authentication flow.

[Calls turingmind_initiate_login]

🧠 TuringMind Login Started

Step 1: Open this URL in your browser:
https://app.turingmind.ai/cli-auth?device_code=...

Step 2: Sign in with Google or GitHub

User: Done!

Claude: [Calls turingmind_poll_login with device_code]

✅ Login Successful!
API key has been saved to ~/.turingmind/config
```

### Review with Upload

```
User: Review my code changes and upload to TuringMind

Claude: [Calls turingmind_validate_auth]
✅ TuringMind Authentication Valid
- Tier: team
- Quota: 495/500 reviews remaining

[Reviews code...]

[Calls turingmind_upload_review]
🧠 Review Uploaded to TuringMind
- Review ID: rev_abc123
- Issues: 3
```

### False Positive Feedback

```
User: That SQL injection issue was a false positive, we use parameterized queries

Claude: [Calls turingmind_submit_feedback]
🚫 Feedback Submitted
Issue iss_abc123 has been marked as false positive.
Pattern saved: `db.query(sql, params)`
This pattern will be skipped in future reviews.
```

## Configuration

### Environment Variables

| Variable | Description | Default |
|----------|-------------|---------|
| `TURINGMIND_API_URL` | API server URL | `https://api.turingmind.ai` |
| `TURINGMIND_API_KEY` | API key | Read from `~/.turingmind/config` |
| `TURINGMIND_DEBUG` | Enable debug logging | `0` |

### Config File

API credentials are stored in `~/.turingmind/config`:

```bash
export TURINGMIND_API_KEY=tmk_your_key_here
export TURINGMIND_API_URL=https://api.turingmind.ai
```

### Claude Desktop with Custom API URL

```json
{
  "mcpServers": {
    "turingmind": {
      "command": "turingmind-mcp",
      "env": {
        "TURINGMIND_API_URL": "https://api.turingmind.ai"
      }
    }
  }
}
```

## Development

### Setup

```bash
git clone https://github.com/turingmindai/turingmind-mcp.git
cd turingmind-mcp
pip install -e ".[dev]"
```

### Run Locally

```bash
python -m turingmind_mcp.server
```

### Test with MCP Inspector

```bash
npx @modelcontextprotocol/inspector turingmind-mcp
```

### Run Tests

```bash
pytest
```

### Lint & Format

```bash
ruff check .
black .
mypy src/
```

## Troubleshooting

### "TURINGMIND_API_KEY not configured"

Run the login flow in Claude, or set the environment variable:

```bash
export TURINGMIND_API_KEY=tmk_your_key_here
```

### "Permission Denied"

API key lacks required permission. Re-run login to create a new key with proper permissions.

### "Connection Error"

1. Check that `TURINGMIND_API_URL` is correct
2. Verify network connectivity
3. For local development, ensure backend is running

### Claude doesn't see the tools

1. Verify `turingmind-mcp` is in your PATH: `which turingmind-mcp`
2. Check Claude Desktop config is valid JSON
3. Restart Claude Desktop completely (Cmd+Q / close from tray)

## License

MIT — see [LICENSE](LICENSE) for details.

## Documentation

### Getting Started
- **[Quick Start Guide](QUICK_START.md)** - Get started in 5 minutes
- **[Platform Guides](docs/platforms/)** - Platform-specific setup

### User Documentation
- **[Features](docs/FEATURES.md)** - Complete feature list and user flows
- **[Compatibility Matrix](docs/COMPATIBILITY_MATRIX.md)** - Platform compatibility

### Developer Documentation
- **[Development Guide](docs/DEVELOPMENT.md)** - Architecture and implementation
- **[Testing Guide](docs/TESTING_GUIDE.md)** - Testing documentation

See [Documentation Index](docs/README.md) for complete documentation structure.

## Links

- [TuringMind](https://turingmind.ai) — AI-powered code review
- [Documentation](https://docs.turingmind.ai)
- [GitHub](https://github.com/turingmindai/turingmind-mcp)
- [Issue Tracker](https://github.com/turingmindai/turingmind-mcp/issues)

TDQS

A3.5/5.0

Scored across 34 tools

Disambiguation4/5

Most tools have clearly distinct names and descriptions, but some overlap exists among state-querying tools like get_decision_queue, get_execution_state, get_ready_nodes, and get_spec_status. Descriptions help disambiguate, but the high number of tools increases potential confusion.

Naming Consistency5/5

All tools follow the consistent turingmind_verb_noun pattern (e.g., list_spec_nodes, apply_edit, classify_failure). Verbs like get, list, create, update, delete are used systematically, making the set predictable and easy to navigate.

Tool Count2/5

With 34 tools, the set exceeds the recommended range of 15-25 for high coherence. While each tool serves a distinct purpose in the complex TuringMind ecosystem, the sheer number overwhelms typical agent memory and selection, making it hard to browse efficiently.

Completeness4/5

The tool surface covers a full lifecycle for constraint-driven development: spec creation, update, promotion, verification, failure classification, repair, and auditing. Minor gaps exist (e.g., no explicit node deletion tool, only update), but the core workflows are well-supported.

Maintenance

ActivityMaintained
ResponsivenessNo issues