🤔 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
- 🧱 Structured Tool Output - Every data-returning tool provides
structuredContent alongside text, per the latest MCP specification
- 🔀 Modern MCP Stack - Built on the official MCP Python SDK v2 (spec 2026-07-28), backwards compatible with older MCP clients
- 🐍 Python Implementation - TypeScript version also available
3️⃣ Start Meilisearch
# Using Docker (recommended)
docker run -d -p 7700:7700 getmeili/meilisearch:v1.28
# 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
🛠️ 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:
MEILI_HTTP_ADDR=http://localhost:7700 # Default Meilisearch URL
MEILI_MASTER_KEY=your_master_key # Optional: Default API key
💻 Development
Setting Up Development Environment
-
Start Meilisearch:
docker run -d -p 7700:7700 getmeili/meilisearch:v1.28
-
Install Development Dependencies:
uv pip install -r requirements-dev.txt
-
Run Tests:
python -m pytest tests/ -v
-
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:
🤗 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 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:
# Using Docker (recommended for development)
docker run -d -p 7700:7700 getmeili/meilisearch:v1.28
# Or using brew (macOS)
brew install meilisearch
meilisearch
# Or download from https://github.com/meilisearch/meilisearch/releases
-
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
- 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
# 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
-
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
-
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
# 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:
- 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:
# 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