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.
Use cases
- Let agents read URLs into markdown without custom scrapers via read_url
- Research workflows combining search_web with sort_by_relevance reranking
- Academic literature scans using search_arxiv from Claude Desktop or Cursor
- Pair with firecrawl-mcp or tavily-search-mcp when comparing web retrieval MCP options
- Filter include_tags=search,read to reduce tool context footprint per Jina docs
Key features
- Claude Desktop
- Cursor
- VS Code
Frequently Asked Questions
- Is this a local or remote MCP server?
- Jina documents a hosted remote server at mcp.jina.ai/v1; clients without remote MCP use the mcp-remote npm proxy.
- Which tools need a Jina API key?
- README marks search_web, search_arxiv, expand_query, sort_by_relevance, and classify_text as requiring a key; read_url is optional-key per the tool table.
- How does this differ from firecrawl-mcp on this site?
- Jina MCP wraps Jina Reader/Search/Embeddings/Reranker APIs; Firecrawl MCP documents crawl/map/scrape tooling for site harvesting—different retrieval stacks.
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.
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.