Meilisearch MCP Server

🤔 What is this?

The Meilisearch MCP Server is a Model Context Protocol server that enables any MCP-compatible client (including Claude, OpenAI agents, and other LLMs) to interact with Meilisearch. This stdio-based server allows AI assistants to manage search indices, perform searches, and handle your data through natural conversation.

Why use this?

• 🤖 Universal Compatibility - Works with any MCP client, not just Claude

• 🗣️ Natural Language Control - Manage Meilisearch through conversation with any LLM

• 🚀 Zero Learning Curve - No need to learn Meilisearch's API

• 🔧 Full Feature Access - All Meilisearch capabilities at your fingertips

• 🔄 Dynamic Connections - Switch between Meilisearch instances on the fly

• 📡 stdio Transport - Currently uses stdio; native Meilisearch MCP support coming soon!

Related MCP server: Documentation MCP Server

✨ Key Features

• 📊 Index & Document Management - Create, update, and manage search indices

• 🔍 Smart Search - Search across single or multiple indices with advanced filtering

• ⚙️ Settings Configuration - Fine-tune search relevancy and performance

• 📈 Task Monitoring - Track indexing progress and system operations

• 🔐 API Key Management - Secure access control

• 🏥 Health Monitoring - Keep tabs on your Meilisearch instance

• 🐍 Python Implementation - TypeScript version also available

🚀 Quick Start

Get up and running in just 3 steps!

1️⃣ Install the package

2️⃣ Configure Claude Desktop

Add this to your claude_desktop_config.json:

3️⃣ Start Meilisearch

That's it! Now you can ask your AI assistant to search and manage your Meilisearch data! 🎉

📚 Examples

💬 Talk to your AI assistant naturally:

🔍 Advanced Search Example:

🔧 Installation

Prerequisites

• Python ≥ 3.9

• Running Meilisearch instance

• MCP-compatible client (Claude Desktop, OpenAI agents, etc.)

From PyPI

From Source (for development)

Using Docker

Perfect for containerized environments like n8n workflows!

From Docker Hub

Build from Source

Integration with n8n

For n8n workflows, you can use the Docker image directly in your setup:

🛠️ What Can You Do?

• View current connection settings

• Switch between Meilisearch instances dynamically

• Update API keys on the fly

• Create new indices with custom primary keys

• List all indices with stats

• Delete indices and their data

• Get detailed index metrics

• Add or update documents

• Retrieve documents with pagination

• Bulk import data

• Search with filters, sorting, and facets

• Multi-index search

• Semantic search with vectors

• Hybrid search (keyword + semantic)

• Configure ranking rules

• Set up faceting and filtering

• Manage searchable attributes

• Customize typo tolerance

• Create and manage API keys

• Set granular permissions

• Monitor key usage

⚠️ Note: While you can add and update hosts and API keys directly in chat for convenience, this approach is primarily designed for development use cases (like connecting to multiple instances on the fly). It does not follow best MCP security practices and should not be used in production environments without proper safeguards.

• Health checks

• System statistics

• Task monitoring

• Version information

🌍 Environment Variables

Configure default connection settings:

💻 Development

Setting Up Development Environment

1. Start Meilisearch:

   docker run -d -p 7700:7700 getmeili/meilisearch:v1.6

2. Install Development Dependencies:

   uv pip install -r requirements-dev.txt

3. Run Tests:

   python -m pytest tests/ -v

4. Format Code:

   black src/ tests/

Testing with MCP Inspector

🤝 Community & Support

We'd love to hear from you! Here's how to get help and connect:

• 💬 Join our Discord - Chat with the community

• 🐛 Report Issues - Found a bug? Let us know!

• 💡 Feature Requests - Have an idea? We're listening!

• 📖 Meilisearch Docs - Learn more about Meilisearch

🤗 Contributing

We welcome contributions! Here's how to get started:

1. Fork the repository

2. Create your feature branch (git checkout -b feature/amazing-feature)

3. Write tests for your changes

4. Make your changes and run tests

5. Format your code with black

6. Commit your changes (git commit -m 'Add amazing feature')

7. Push to your branch (git push origin feature/amazing-feature)

8. Open a Pull Request

See our Contributing Guidelines for more details.

📦 Release Process

This project uses automated versioning and publishing. When the version in pyproject.toml changes on the main branch, the package is automatically published to PyPI.

See the Release Process section for detailed instructions.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Available Tools

Connection Management

• get-connection-settings: View current Meilisearch connection URL and API key status

• update-connection-settings: Update URL and/or API key to connect to a different instance

Index Management

• create-index: Create a new index with optional primary key

• list-indexes: List all available indexes

• delete-index: Delete an existing index and all its documents

• get-index-metrics: Get detailed metrics for a specific index

Document Operations

• get-documents: Retrieve documents from an index with pagination

• add-documents: Add or update documents in an index

Search

• search: Flexible search across single or multiple indices with filtering and sorting options

Settings Management

• get-settings: View current settings for an index

• update-settings: Update index settings (ranking, faceting, etc.)

API Key Management

• get-keys: List all API keys

• create-key: Create new API key with specific permissions

• delete-key: Delete an existing API key

Task Management

• get-task: Get information about a specific task

• get-tasks: List tasks with optional filters

• cancel-tasks: Cancel pending or enqueued tasks

• delete-tasks: Delete completed tasks

System Monitoring

• health-check: Basic health check

• get-health-status: Comprehensive health status

• get-version: Get Meilisearch version information

• get-stats: Get database statistics

• get-system-info: Get system-level information

Development Setup

Prerequisites

1. Start Meilisearch server:

   # Using Docker (recommended for development) docker run -d -p 7700:7700 getmeili/meilisearch:v1.6 # Or using brew (macOS) brew install meilisearch meilisearch # Or download from https://github.com/meilisearch/meilisearch/releases

2. Install development tools:

   # Install uv for Python package management pip install uv # Install Node.js for MCP Inspector testing # Visit https://nodejs.org/ or use your package manager

Running Tests

This project includes comprehensive integration tests that verify MCP tool functionality:

Important: Tests require a running Meilisearch instance on http://localhost:7700.

Code Quality

Contributing Guidelines

1. Fork and clone the repository

2. Set up development environment following the Development Setup section above

3. Create a feature branch from main

4. Write tests first if adding new functionality (Test-Driven Development)

5. Run tests locally to ensure all tests pass before committing

6. Format code with Black and ensure code quality

7. Commit changes with descriptive commit messages

8. Push to your fork and create a pull request

Development Workflow

Testing Guidelines

• All new features should include tests

• Tests should pass before submitting PRs

• Use descriptive test names and clear assertions

• Test both success and error cases

• Ensure Meilisearch is running before running tests

Release Process

This project uses automated versioning and publishing to PyPI. The release process is designed to be simple and automated.

How Releases Work

1. Automated Publishing: When the version number in pyproject.toml changes on the main branch, a GitHub Action automatically:

   • Builds the Python package

   • Publishes it to PyPI using trusted publishing

   • Creates a new release on GitHub

2. Version Detection: The workflow compares the current version in pyproject.toml with the previous commit to detect changes

3. PyPI Publishing: Uses PyPA's official publish action with trusted publishing (no manual API keys needed)

Creating a New Release

To create a new release, follow these steps:

1. Determine Version Number

Follow Semantic Versioning (MAJOR.MINOR.PATCH):

• PATCH (e.g., 0.4.0 → 0.4.1): Bug fixes, documentation updates, minor improvements

• MINOR (e.g., 0.4.0 → 0.5.0): New features, new MCP tools, significant enhancements

• MAJOR (e.g., 0.5.0 → 1.0.0): Breaking changes, major API changes

2. Update Version and Create PR

3. Merge to Main

Once the PR is approved and merged to main, the GitHub Action will automatically:

1. Detect the version change

2. Build the package

3. Publish to PyPI at https://pypi.org/p/meilisearch-mcp

4. Make the new version available via pip install meilisearch-mcp

4. Verify Release

After merging, verify the release:

Release Workflow File

The automated release is handled by .github/workflows/publish.yml, which:

• Triggers on pushes to main branch

• Checks if pyproject.toml version changed

• Uses Python 3.10 and official build tools

• Publishes using trusted publishing (no API keys required)

• Provides verbose output for debugging

Troubleshooting Releases

Release didn't trigger: Check that the version in pyproject.toml actually changed between commits

Build failed: Check the GitHub Actions logs for Python package build errors

PyPI publish failed: Verify the package name and that trusted publishing is configured properly

Version conflicts: Ensure the new version number hasn't been used before on PyPI

Development vs Production Versions

• Development: Install from source using pip install -e .

• Production: Install from PyPI using pip install meilisearch-mcp

• Specific version: Install using pip install meilisearch-mcp==0.5.0