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.
Use cases
- Let agents run hybrid or vector Typesense searches with collection-scoped keys
- Inspect schemas via collection:// resources before drafting YQL-like filter queries
- Use readonly production sources for safe analytics while staging sources allow manage writes
- Compare typesense-mcp against meilisearch-mcp or algolia-productivity-mcp during evaluations
- Prototype agent search QA over timestamped collection prefixes in TOML config
Key features
- Claude Desktop
- Cursor
- VS Code
Frequently Asked Questions
- Is this an official Typesense MCP?
- No—Typesense does not list fogx/typesense-mcp as an official server; it is a community MCP documented in the GitHub README.
- How do I prevent agents from deleting collections?
- Set `readonly = true` on production sources or scope collections patterns; manage is omitted entirely if all sources are readonly.
- Does it support hybrid search?
- README documents the search tool for text, vector (by embedding or document ID), and hybrid search with alpha blending.
Related
Related
3 Indexed items
Meilisearch MCP Server
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.
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.
OpenSearch MCP Server
OpenSearch documents an open-source Model Context Protocol server at docs.opensearch.org/latest/ai-agent-integrations/mcp-server for AI assistants to interact with OpenSearch clusters via MCP tools instead of raw REST. The opensearch-project/opensearch-mcp-server-py package supports stdio (Claude Desktop, Cursor, Kiro) and streaming transports (SSE/Streamable HTTP) with tools for listing indexes, retrieving mappings, running search queries, checking cluster health, and counting documents per docs. Configure single-cluster mode via environment variables or multi-cluster YAML; authentication supports basic auth, IAM, header auth, and mTLS for self-managed OpenSearch, Amazon OpenSearch Service, and Serverless. OpenSearch 3.0+ also ships an experimental in-cluster MCP endpoint at `/_plugins/_ml/mcp` (Streamable HTTP) per ML Commons docs—distinct from the standalone py server for external clients.