Meilisearch maintains an official Model Context Protocol server in meilisearch/meilisearch-mcp, documented at meilisearch.com/blog/introducing-mcp-server. The Python stdio server connects MCP clients to any running Meilisearch instance via `MEILI_HTTP_ADDR` and optional `MEILI_MASTER_KEY`, with `update-connection-settings` to switch hosts mid-session. Tools cover index management, document ingestion, search (filters, sorting, facets, semantic/hybrid), settings, API keys, tasks, and health checks per the README. Install paths include `uvx meilisearch-mcp`, pip, source, and Docker (`getmeili/meilisearch-mcp`). Meilisearch notes the server is development-oriented and that native Meilisearch MCP transport support is coming.
Use cases
- Let Cursor agents create Meilisearch indexes and ingest documents conversationally
- Prototype hybrid/semantic search settings without memorizing Meilisearch REST APIs
- Monitor Meilisearch tasks and health from an MCP client during development
- Switch between local and Meilisearch Cloud instances via update-connection-settings
- Pair with turbopuffer or typesense evaluations when comparing search backends
Key features
- Claude Desktop
- Cursor
- OpenAI agents
- VS Code
Frequently Asked Questions
- Does this replace Meilisearch itself?
- No—the MCP server is a control layer; you still need a self-hosted or Meilisearch Cloud search instance.
- Is it production-ready?
- Meilisearch blog describes the MCP server as development-oriented; destructive tools like delete-index need safeguards in prod.
- Which transport is supported?
- README documents stdio today; Meilisearch notes native MCP transport support is coming.
Related
Related
3 Indexed items
Algolia Productivity MCP Server
Algolia documents an official managed Model Context Protocol server at algolia.com/doc/guides/model-context-protocol/productivity-mcp. Connect MCP clients to the remote HTTP endpoint `https://mcp.algolia.com/mcp` with OAuth (enable under Generate AI in the Algolia dashboard; sign in when prompted so the MCP inherits your account permissions). Productivity MCP is user-scoped and read-only per docs—tools cover search (`algolia_search_list_indices`, `algolia_search_index`, `algolia_search_for_facet_values`), Recommend (`algolia_recommendations`), and analytics helpers such as top searches, no-click rates, filter usage, and user counts. Algolia docs distinguish this from Algolia Public MCP for application-scoped, curated index exposure to external agents. Supported clients include ChatGPT, Claude, Claude Code, Cursor, Gemini CLI, VS Code, and OpenAI Playground.
Typesense MCP Server
The fogx/typesense-mcp project provides a community Model Context Protocol server for querying and managing Typesense search indices via stdio. Configure a TOML file with one or more `[[sources]]` blocks (id, host, api_key, collections patterns, optional readonly, port, protocol) and launch with `npx -y typesense-mcp path/to/typesense-mcp.toml` per the README. Tools include `search` for full-text, vector, or hybrid search; `lookup` for collections, schema, documents, counts, aliases, and synonyms; and `manage` for upserts, deletes, collection/synonym/alias writes when at least one source is not readonly. Collection patterns gate access; readonly sources block writes and hide `manage` when all sources are readonly. A `collection://{source}/{collection}` resource exposes field schemas. Pairs naturally with the Typesense tool entry on this site for agent-driven index exploration.
Jina AI MCP Server
Jina AI documents an official remote Model Context Protocol server in the jina-ai/MCP repository at https://mcp.jina.ai/v1 using Streamable HTTP transport (MCP spec 2025-03-26). Tools expose Jina Reader, Embeddings, and Reranker APIs: primer for contextual status; read_url and parallel_read_url for URL-to-markdown extraction; capture_screenshot_url and guess_datetime_url for page screenshots and publish-date hints; search_web, search_arxiv, search_ssrn, and search_images for web and specialized search; expand_query for query rewriting; sort_by_relevance for reranking; classify_text and deduplicate_strings for embedding-powered text tasks per the README. Clients with native remote MCP support connect directly with Authorization Bearer JINA_API_KEY; others use npx mcp-remote https://mcp.jina.ai/v1. Optional URL filters include_tags/exclude_tags to trim tool lists. Pairs with the jina-ai tool entry for agent-driven reading and search workflows.