Retrieve Meilisearch version details via the Meilisearch MCP Server to ensure compatibility and monitor system updates.
MIT
385,451 tools. Last updated 2026-08-03 21:09
"Meilisearch" matching MCP tools:
- Retrieve all indexes from a Meilisearch server to manage and organize search data efficiently. Ideal for maintaining structured search environments.MIT
- Retrieve comprehensive health status details for Meilisearch, enabling proactive monitoring and troubleshooting through the Meilisearch MCP Server.MIT
- Retrieve current Meilisearch connection settings for integration and configuration with LLM interfaces like Claude.MIT
- Monitor the operational status of Meilisearch servers to ensure uptime and performance. Use this tool to verify server health and diagnose potential issues efficiently.MIT
- Remove a specified Meilisearch index using its unique identifier (UID) to manage search data effectively.MIT
Matching MCP Servers
- AlicenseBqualityDmaintenanceServer for interacting with Meilisearch through LLM interfaces like Claude.Last updated22194MIT
- Modify Meilisearch connection settings, including URL and API key, to ensure proper integration with LLM interfaces via the MCP server.MIT
- Generates a new Meilisearch index by specifying a unique identifier (uid) and optional primary key, enabling efficient data organization and search functionality.MIT
- Check the operational status of the Meilisearch server to verify its availability and functionality for search operations.MIT
- Retrieve the current version details of the connected Meilisearch server to verify compatibility and monitor updates.MIT
- Retrieve the current version details of the connected Meilisearch server to verify compatibility and monitor updates.
- Check if the Meilisearch server is operational and responding to requests to ensure search functionality is available.
- Reset all settings for a Meilisearch index to their default values using the index's unique identifier.MIT
- Activate vector search capabilities in Meilisearch to enable semantic similarity-based document retrieval using AI embeddings.MIT
- Modify a Meilisearch index by changing its primary key to organize data more effectively for search operations.
- Remove multiple documents by their IDs from a Meilisearch index to manage data and maintain search relevance.MIT
- Retrieve system information from the Meilisearch server to monitor its status and configuration.MIT
- Remove multiple documents by their IDs from a Meilisearch index to manage data and maintain search relevance.
- Activate vector search functionality in Meilisearch to enable semantic similarity-based document retrieval.
- Check the activation status of experimental features in Meilisearch to determine which advanced capabilities are available for use.