This server provides a Model Context Protocol (MCP) interface for Meilisearch, enabling seamless integration with LLM tools like Claude. You can:
Manage Connections: View and update Meilisearch URL/API key settings
Search Functionality: Perform flexible searches across indices with filtering, sorting, limiting, and offset
Index Management: Create, list, and retrieve metrics for indices
Document Operations: Add, retrieve, and manage documents
Settings Management: View and update index settings like ranking and faceting
API Key Management: Create, list, and delete API keys with specific permissions
Task Management: Monitor, cancel, and delete tasks with detailed filtering options
System Monitoring: Check health, view version information, and get database statistics
Provides a Model Context Protocol (MCP) server for interacting with Meilisearch through LLM interfaces, supporting index and document management, settings configuration, task monitoring, API key management, and search capabilities across single or multiple indices.
🤔 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
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
- Health checks
- System statistics
- Task monitoring
- Version information
🌍 Environment Variables
Configure default connection settings:
💻 Development
Setting Up Development Environment
- Start Meilisearch:
- Install Development Dependencies:
- Run Tests:
- Format Code:
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:
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature
) - Write tests for your changes
- Make your changes and run tests
- Format your code with
black
- Commit your changes (
git commit -m 'Add amazing feature'
) - Push to your branch (
git push origin feature/amazing-feature
) - 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 statusupdate-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 keylist-indexes
: List all available indexesdelete-index
: Delete an existing index and all its documentsget-index-metrics
: Get detailed metrics for a specific index
Document Operations
get-documents
: Retrieve documents from an index with paginationadd-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 indexupdate-settings
: Update index settings (ranking, faceting, etc.)
API Key Management
get-keys
: List all API keyscreate-key
: Create new API key with specific permissionsdelete-key
: Delete an existing API key
Task Management
get-task
: Get information about a specific taskget-tasks
: List tasks with optional filterscancel-tasks
: Cancel pending or enqueued tasksdelete-tasks
: Delete completed tasks
System Monitoring
health-check
: Basic health checkget-health-status
: Comprehensive health statusget-version
: Get Meilisearch version informationget-stats
: Get database statisticsget-system-info
: Get system-level information
Development Setup
Prerequisites
- Start Meilisearch server:
- Install development tools:
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
- Fork and clone the repository
- Set up development environment following the Development Setup section above
- Create a feature branch from
main
- Write tests first if adding new functionality (Test-Driven Development)
- Run tests locally to ensure all tests pass before committing
- Format code with Black and ensure code quality
- Commit changes with descriptive commit messages
- 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
- Automated Publishing: When the version number in
pyproject.toml
changes on themain
branch, a GitHub Action automatically:- Builds the Python package
- Publishes it to PyPI using trusted publishing
- Creates a new release on GitHub
- Version Detection: The workflow compares the current version in
pyproject.toml
with the previous commit to detect changes - 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:
- Detect the version change
- Build the package
- Publish to PyPI at https://pypi.org/p/meilisearch-mcp
- 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
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
Tools
Server for interacting with Meilisearch through LLM interfaces like Claude.
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