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Meilisearch MCP Server

Local
by: meilisearch
|
category: Databases
|
2025.07.04 updated

A Model Context Protocol (MCP) server for interacting with Meilisearch through LLM interfaces like Claude.

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Meilisearch

Meilisearch MCP Server

Meilisearch | Meilisearch Cloud | Documentation | Discord

PyPI version Python Versions Tests License Downloads

⚡ Connect any LLM to Meilisearch and supercharge your AI with lightning-fast search capabilities! 🔍

🤔 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!

✨ 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

# Using pip
pip install meilisearch-mcp

# Or using uvx (recommended)
uvx -n meilisearch-mcp

2️⃣ Configure Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "meilisearch": {
      "command": "uvx",
      "args": ["-n", "meilisearch-mcp"]
    }
  }
}

3️⃣ Start Meilisearch

# Using Docker (recommended)
docker run -d -p 7700:7700 getmeili/meilisearch:v1.6

# Or using Homebrew
brew install meilisearch
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:

You: "Create a new index called 'products' with 'id' as the primary key"
AI: I'll create that index for you... ✓ Index 'products' created successfully!

You: "Add some products to the index"
AI: I'll add those products... ✓ Added 5 documents to 'products' index

You: "Search for products under $50 with 'electronics' in the category"
AI: I'll search for those products... Found 12 matching products!

🔍 Advanced Search Example:

You: "Search across all my indices for 'machine learning' and sort by date"
AI: Searching across all indices... Found 47 results from 3 indices:
- 'blog_posts': 23 articles about ML
- 'documentation': 15 technical guides  
- 'tutorials': 9 hands-on tutorials

🔧 Installation

Prerequisites

  • Python ≥ 3.9
  • Running Meilisearch instance
  • MCP-compatible client (Claude Desktop, OpenAI agents, etc.)

From PyPI

pip install meilisearch-mcp

From Source (for development)

# Clone repository
git clone https://github.com/meilisearch/meilisearch-mcp.git
cd meilisearch-mcp

# Create virtual environment and install
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -e .

Using Docker

Perfect for containerized environments like n8n workflows!

From Docker Hub

# Pull the latest image
docker pull getmeili/meilisearch-mcp:latest

# Or a specific version
docker pull getmeili/meilisearch-mcp:0.5.0

# Run the container
docker run -it \
  -e MEILI_HTTP_ADDR=http://your-meilisearch:7700 \
  -e MEILI_MASTER_KEY=your-master-key \
  getmeili/meilisearch-mcp:latest

Build from Source

# Build your own image
docker build -t meilisearch-mcp .
docker run -it \
  -e MEILI_HTTP_ADDR=http://your-meilisearch:7700 \
  -e MEILI_MASTER_KEY=your-master-key \
  meilisearch-mcp

Integration with n8n

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

meilisearch-mcp:
  image: getmeili/meilisearch-mcp:latest
  environment:
    - MEILI_HTTP_ADDR=http://meilisearch:7700
    - MEILI_MASTER_KEY=masterKey

🛠️ What Can You Do?

🔗 Connection Management
  • View current connection settings
  • Switch between Meilisearch instances dynamically
  • Update API keys on the fly
📁 Index Operations
  • Create new indices with custom primary keys
  • List all indices with stats
  • Delete indices and their data
  • Get detailed index metrics
📄 Document Management
  • Add or update documents
  • Retrieve documents with pagination
  • Bulk import data
🔍 Search Capabilities
  • Search with filters, sorting, and facets
  • Multi-index search
  • Semantic search with vectors
  • Hybrid search (keyword + semantic)
⚙️ Settings & Configuration
  • Configure ranking rules
  • Set up faceting and filtering
  • Manage searchable attributes
  • Customize typo tolerance
🔐 Security
  • Create and manage API keys
  • Set granular permissions
  • Monitor key usage
📊 Monitoring & Health
  • Health checks
  • System statistics
  • Task monitoring
  • Version information

🌍 Environment Variables

Configure default connection settings:

MEILI_HTTP_ADDR=http://localhost:7700  # Default Meilisearch URL
MEILI_MASTER_KEY=your_master_key       # Optional: Default API key

💻 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

npx @modelcontextprotocol/inspector python -m src.meilisearch_mcp

🤝 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.

Meilisearch is an open-source search engine that offers a delightful search experience.
Learn more about Meilisearch at meilisearch.com

📖 Full Documentation

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:

# Run all tests
python -m pytest tests/ -v

# Run specific test file
python -m pytest tests/test_mcp_client.py -v

# Run tests with coverage report
python -m pytest --cov=src tests/

# Run tests in watch mode (requires pytest-watch)
pytest-watch tests/

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

Code Quality

# Format code with Black
black src/ tests/

# Run type checking (if mypy is configured)
mypy src/

# Lint code (if flake8 is configured)
flake8 src/ tests/

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

# Create feature branch
git checkout -b feature/your-feature-name

# Make your changes, write tests first
# Edit files...

# Run tests to ensure everything works
python -m pytest tests/ -v

# Format code
black src/ tests/

# Commit and push
git add .
git commit -m "Add feature description"
git push origin feature/your-feature-name

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
# 1. Create a branch from latest main
git checkout main
git pull origin main
git checkout -b release/v0.5.0

# 2. Update version in pyproject.toml
# Edit the version = "0.4.0" line to your new version

# 3. Commit and push
git add pyproject.toml
git commit -m "Bump version to 0.5.0"
git push origin release/v0.5.0

# 4. Create PR and get it reviewed/merged
gh pr create --title "Release v0.5.0" --body "Bump version for release"
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:

# Check GitHub Action status
gh run list --workflow=publish.yml

# Verify on PyPI (may take a few minutes)
pip index versions meilisearch-mcp

# Test installation of new version
pip install --upgrade meilisearch-mcp

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

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