Meilisearch MCP Server

Meilisearch MCP Server avatar

by tpayet

Official Integrations

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

What is Meilisearch MCP Server

Meilisearch MCP Server

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

Features

Installation

# Clone repository
git clone <repository_url>
cd meilisearch-mcp

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

Requirements

  • Python โ‰ฅ 3.9
  • Running Meilisearch instance
  • Node.js (for testing with MCP Inspector)

Usage

Environment Variables

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

Dynamic Connection Configuration

The server provides tools to view and update connection settings at runtime:

  • get-connection-settings: View current connection URL and API key status
  • update-connection-settings: Update URL and/or API key to connect to a different Meilisearch instance

Example usage through MCP:

// Get current settings
{
  "name": "get-connection-settings"
}

// Update connection settings
{
  "name": "update-connection-settings",
  "arguments": {
    "url": "http://new-host:7700",
    "api_key": "new-api-key"
  }
}

Search Functionality

The server provides a flexible search tool that can search across one or all indices:

  • search: Search through Meilisearch indices with optional parameters

Example usage through MCP:

// Search in a specific index
{
  "name": "search",
  "arguments": {
    "query": "search term",
    "indexUid": "movies",
    "limit": 10
  }
}

// Search across all indices
{
  "name": "search",
  "arguments": {
    "query": "search term",
    "limit": 5,
    "sort": ["releaseDate:desc"]
  }
}

Available search parameters:

  • query: The search query (required)
  • indexUid: Specific index to search in (optional)
  • limit: Maximum number of results per index (optional, default: 20)
  • offset: Number of results to skip (optional, default: 0)
  • filter: Filter expression (optional)
  • sort: Sorting rules (optional)

Running the Server

python -m src.meilisearch_mcp

Usage with Claude Desktop

To use this with Claude Desktop, add the following to your claude_desktop_config.json:

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

Testing with MCP Inspector

npx @modelcontextprotocol/inspector python -m src.meilisearch_mcp

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
  • 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:
    • limit: Maximum number of tasks to return
    • from: Number of tasks to skip
    • reverse: Sort order of tasks
    • batchUids: Filter by batch UIDs
    • uids: Filter by task UIDs
    • canceledBy: Filter by cancellation source
    • types: Filter by task types
    • statuses: Filter by task statuses
    • indexUids: Filter by index UIDs
    • afterEnqueuedAt/beforeEnqueuedAt: Filter by enqueue time
    • afterStartedAt/beforeStartedAt: Filter by start time
    • afterFinishedAt/beforeFinishedAt: Filter by finish time
  • 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

Contributing

  1. Fork repository
  2. Create feature branch
  3. Commit changes
  4. Create pull request

License

MIT

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Frequently Asked Questions

What is MCP?

MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications, providing a standardized way to connect AI models to different data sources and tools.

What are MCP Servers?

MCP Servers are lightweight programs that expose specific capabilities through the standardized Model Context Protocol. They act as bridges between LLMs like Claude and various data sources or services, allowing secure access to files, databases, APIs, and other resources.

How do MCP Servers work?

MCP Servers follow a client-server architecture where a host application (like Claude Desktop) connects to multiple servers. Each server provides specific functionality through standardized endpoints and protocols, enabling Claude to access data and perform actions through the standardized protocol.

Are MCP Servers secure?

Yes, MCP Servers are designed with security in mind. They run locally with explicit configuration and permissions, require user approval for actions, and include built-in security features to prevent unauthorized access and ensure data privacy.