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.
Use cases
- Let agents query Algolia indices and facet values during relevance tuning
- Pull no-click and zero-result analytics into LLM-assisted search reviews
- Compare Productivity MCP against meilisearch-mcp for hybrid search ops
- Prototype internal search QA workflows without writing custom analytics scripts
- Pair with algolia tool entry when evaluating Public MCP for external agents
Key features
- Claude Desktop
- Cursor
- ChatGPT
- VS Code
- Gemini CLI
Frequently Asked Questions
- Is this the same as algolia/mcp-node on GitHub?
- No—Algolia docs describe managed Public and Productivity MCP servers; mcp-node is an experimental local Node server not covered by Algolia SLA.
- Can agents write to indices?
- Productivity MCP docs describe read-only access for analysis and exploration; data changes still happen through Algolia APIs outside MCP.
- Public MCP or Productivity MCP?
- Algolia docs: Public MCP for curated external agent access; Productivity MCP for internal user-scoped workflows across your applications.
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.
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.
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.