Meilisearch MCP Server

What does “Meilisearch” MCP Server do?

The Meilisearch MCP Server is a Model Context Protocol (MCP) server that enables seamless interaction between AI assistants (such as LLMs) and a Meilisearch instance. Acting as a bridge, it allows AI-driven clients to perform database operations—like managing indexes, documents, settings, and API keys—directly from their interface. The server supports dynamic connection configuration, built-in logging, and smart search across indices. This integration streamlines workflows for developers, letting them automate and monitor search infrastructure tasks using natural language or workflow automation tools.

List of Prompts

No prompt templates are directly mentioned in the repository or documentation.

List of Resources

No explicit MCP resources are described in the available documentation or files.

List of Tools

• Index and document management
  Allows clients to create, update, delete, and manage Meilisearch indexes and documents through exposed functions.
• Settings configuration and management
  Enables configuration of index settings and other operational parameters via MCP-exposed actions.
• Task monitoring and API key management
  Provides functionality to monitor running tasks and manage API keys for Meilisearch securely.
• Dynamic connection configuration
  Tools to view and update the Meilisearch connection URL and API key at runtime (get-connection-settings).

Use Cases of this MCP Server

• Database Index and Document Management
  Developers can automate the creation, updating, and deletion of search indexes and documents, streamlining content management tasks.
• Search Settings Configuration
  Adjust index settings (like ranking rules or synonyms) programmatically, enabling rapid experimentation and optimization.
• API Key Management
  Securely create, revoke, and rotate API keys for Meilisearch, supporting robust access control in production environments.
• Task Monitoring
  Track the status of ongoing tasks (like index updates) to ensure operations complete successfully and handle errors proactively.
• Dynamic Multi-Instance Switching
  Instantly switch between different Meilisearch instances using dynamic connection tools, supporting multi-environment development and testing.

How to set it up

Windsurf

1. Ensure you have Node.js and Python ≥ 3.9 installed.
2. Start your Meilisearch instance and note its HTTP address and API key.
3. Edit your Windsurf configuration file to add the Meilisearch MCP server.
4. Add the MCP server configuration using the following JSON snippet:

   "mcpServers": {
     "meilisearch-mcp": {
       "command": "meilisearch-mcp",
       "args": [],
       "env": {
         "MEILI_HTTP_ADDR": "http://localhost:7700",
         "MEILI_MASTER_KEY": "your_master_key"
       }
     }
   }
   

5. Save the configuration and restart Windsurf. Verify the MCP server connectivity.

Claude

1. Make sure Python ≥ 3.9 is installed and Meilisearch is running.
2. Refer to the CLAUDE.md file for additional Claude-specific integration steps if available.
3. Add the following configuration in your Claude setup:

   "mcpServers": {
     "meilisearch-mcp": {
       "command": "meilisearch-mcp",
       "args": [],
       "env": {
         "MEILI_HTTP_ADDR": "http://localhost:7700",
         "MEILI_MASTER_KEY": "your_master_key"
       }
     }
   }
   

4. Restart Claude and ensure the MCP server is detected.

Cursor

1. Install Node.js and Python ≥ 3.9, and verify Meilisearch is running.
2. Open your Cursor configuration file and add the MCP server entry:

   "mcpServers": {
     "meilisearch-mcp": {
       "command": "meilisearch-mcp",
       "args": [],
       "env": {
         "MEILI_HTTP_ADDR": "http://localhost:7700",
         "MEILI_MASTER_KEY": "your_master_key"
       }
     }
   }
   

3. Save and restart Cursor. Confirm server functionality.

Cline

1. Prepare your environment with Python ≥ 3.9 and a running Meilisearch instance.
2. Edit the Cline configuration to include the MCP server as follows:

   "mcpServers": {
     "meilisearch-mcp": {
       "command": "meilisearch-mcp",
       "args": [],
       "env": {
         "MEILI_HTTP_ADDR": "http://localhost:7700",
         "MEILI_MASTER_KEY": "your_master_key"
       }
     }
   }
   

3. Save changes and restart Cline. Test connectivity.

Securing API Keys
Always use environment variables to store sensitive data like API keys. Example:

"env": {
  "MEILI_HTTP_ADDR": "http://localhost:7700",
  "MEILI_MASTER_KEY": "your_master_key"
}

You can also use "inputs" if supported by your platform for additional runtime secrets.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "meilisearch-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “meilisearch-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

The Meilisearch MCP server provides robust tooling for Meilisearch automation and LLM integration, with comprehensive setup and security documentation. However, the absence of explicit prompt templates/resources and unclear support for Roots/Sampling slightly limits its flexibility for some advanced use cases.

MCP Score

Rating:
I would rate this MCP server a solid 7/10. It covers critical developer needs for Meilisearch automation and LLM workflows, with clear documentation and support for key tools, but lacks some advanced MCP features and explicit resource/prompt definitions.