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Remote MCP server providing access to Reader, Embeddings, and Reranker APIs with web/search tools.
Configuration
View docs{
"mcpServers": {
"zhijiew-jina-mcp": {
"url": "https://mcp.jina.ai/sse",
"headers": {
"JINA_API_KEY": "YOUR_API_KEY"
}
}
}
}You run a remote MCP server from Jina that gives you access to Reader, Embeddings, and Reranker APIs through convenient MCP endpoints. It’s designed to be easy to connect to from MCP clients, supports both HTTP-based remote access and local stdio proxies, and works with optional API keys for higher rate limits and better performance.
To connect your MCP-enabled client to the Jina remote MCP server, choose the preferred connection method and configure your client accordingly. You can either use the remote HTTP endpoint or run a local stdio proxy that forwards to the remote MCP server. Both options let you access a suite of tools for web content, search, and embeddings.
Option 1: HTTP remote MCP endpoint (recommended if your client supports direct remote connections). You’ll point your client to the remote SSE URL and optionally provide an API key in the Authorization header.
Option 2: Local stdio proxy (used when your client does not yet support remote MCP servers). You’ll run a local proxy that forwards to the remote MCP SSE URL and passes your API key in the headers.
Prerequisites: you need Node.js and npm installed on your system. You’ll also need an API key if you want higher rate limits and access to paid features.
Install and run the HTTP remote MCP server config by connecting your client directly to the remote SSE URL.
Some tools require a Jina API key for higher rate limits and broader access. You can obtain a free API key from jina.ai and pass it in the Authorization header as a Bearer token when you configure your MCP client.
If you encounter issues with tool calling loops, content truncation, or disconnections, first verify that your API key is valid and that your MCP client supports the chosen connection method. If a client shows a UI error while using a local proxy, try restarting the proxy to refresh tool definitions.
The server exposes a wide set of tools for web content extraction, search, and embeddings. When you integrate, consider using parallel tool variants for faster results and combining URL reads with searches to ensure you get sourced content alongside structured results.
{
"mcpServers": {
"jina-mcp-server": {
"url": "https://mcp.jina.ai/sse",
"headers": {
"Authorization": "Bearer ${JINA_API_KEY}"
}
}
}
}{
"mcpServers": {
"jina-mcp-server": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.jina.ai/sse",
"--header",
"Authorization: Bearer ${JINA_API_KEY}"
]
}
}
}The MCP server provides a suite of tools to fetch, parse, and search content, including contextual info, web/text extraction, and various search endpoints. You can enable/disable tools as needed in your client configuration and combine multiple tools to build rich conversational flows.
Get current contextual information for localized, time-aware responses
Extract clean, structured content from web pages as markdown via Reader API (optional API key)
Capture high-quality screenshots of web pages via Reader API (optional API key)
Analyze web pages for last update/publish datetime with confidence scores
Search the entire web for current information and news via Reader API (requires API key)
Search academic papers on arXiv via Reader API (requires API key)
Search SSRN papers via Reader API (requires API key)
Search images across the web via Reader API (requires API key)
Expand and rewrite search queries using the query expansion model via Reader API (requires API key)
Read multiple web pages in parallel for efficient content extraction via Reader API (optional)
Run multiple web searches in parallel for broad topic coverage via Reader API (requires API key)
Run multiple arXiv searches in parallel for comprehensive coverage (requires API key)
Run multiple SSRN searches in parallel for social science coverage (requires API key)
Rerank documents by relevance to a query via Reranker API (requires API key)
Find top-k semantically unique strings via Embeddings API and submodular optimization (requires API key)
Find top-k semantically unique images via Embeddings API and submodular optimization (requires API key)