Jina AI Remote MCP Server

A remote Model Context Protocol (MCP) server that provides access to Jina Reader, Embeddings and Reranker APIs with a suite of URL-to-markdown, web search, image search, and embeddings/reranker tools:

Tool	Description	Is Jina API Key Required?
primer	Get current contextual information for localized, time-aware responses	No
read_url	Extract clean, structured content from web pages as markdown via Reader API	Optional*
capture_screenshot_url	Capture high-quality screenshots of web pages via Reader API	Optional*
guess_datetime_url	Analyze web pages for last update/publish datetime with confidence scores	No
search_web	Search the entire web for current information and news via Reader API	Yes
search_arxiv	Search academic papers and preprints on arXiv repository via Reader API	Yes
search_ssrn	Search academic papers on SSRN (Social Science Research Network) via Reader API	Yes
search_images	Search for images across the web (similar to Google Images) via Reader API	Yes
expand_query	Expand and rewrite search queries based on the query expansion model via Reader API	Yes
parallel_read_url	Read multiple web pages in parallel for efficient content extraction via Reader API	Optional*
parallel_search_web	Run multiple web searches in parallel for comprehensive topic coverage and diverse perspectives via Reader API	Yes
parallel_search_arxiv	Run multiple arXiv searches in parallel for comprehensive research coverage and diverse academic angles via Reader API	Yes
parallel_search_ssrn	Run multiple SSRN searches in parallel for comprehensive social science research coverage via Reader API	Yes
sort_by_relevance	Rerank documents by relevance to a query via Reranker API	Yes
deduplicate_strings	Get top-k semantically unique strings via Embeddings API and submodular optimization	Yes
deduplicate_images	Get top-k semantically unique images via Embeddings API and submodular optimization	Yes

Optional tools work without an API key but have rate limits. For higher rate limits and better performance, use a Jina API key. You can get a free Jina API key from https://jina.ai

Usage

Warning

Some clients do not support env variable, so you may need to replace ${JINA_API_KEY} below to a hardcoded real API key jina_xxx.

For client that supports remote MCP server:

{
  "mcpServers": {
    "jina-mcp-server": {
      "url": "https://mcp.jina.ai/sse",
      "headers": {
        "Authorization": "Bearer ${JINA_API_KEY}" // optional
      }
    }
  }
}

For client that does not support remote MCP server yet, you need mcp-remote a local proxy to connect to the remote MCP server.

{
  "mcpServers": {
    "jina-mcp-server": {
      "command": "npx",
      "args": [
        "mcp-remote", 
        "https://mcp.jina.ai/sse"
        "--header",
        "Authorization: Bearer ${JINA_API_KEY}"
        ]
    }
  }
}

For Claude Code:

claude mcp add --transport sse jina https://mcp.jina.ai/sse \
  --header "Authorization : Bearer ${JINA_API_KEY}"

For OpenAI Codex: find ~/.codex/config.toml and add the following:

[mcp_servers.jina-mcp-server]
command = "npx"
args = [
    "-y",
    "mcp-remote",
    "https://mcp.jina.ai/sse",
    "--header",
    "Authorization: Bearer ${JINA_API_KEY}"]

Tool Filtering before Registering

Every MCP tool requires the LLM to pre-allocate tokens in its context window for the tool's name, description, and schema. For LLMs with limited context windows, registering all 17 tools can consume significant space before any actual work begins.

By filtering tools server-side via query parameters on the endpoint URL (/sse?...), excluded tools are never registered with the MCP client. The client and LLM never see them, saving context window for what matters.

Query Parameters

Parameter	Description	Example
exclude_tools	Comma-separated tool names to exclude	exclude_tools=search_web,search_arxiv
include_tools	Comma-separated tool names to include	include_tools=read_url,search_web
exclude_tags	Comma-separated tags to exclude	exclude_tags=parallel,rerank
include_tags	Comma-separated tags to include	include_tags=search,read

Available Tags

Tag	Tools
search	search_web, search_arxiv, search_ssrn, search_images
parallel	parallel_search_web, parallel_search_arxiv, parallel_search_ssrn, parallel_read_url
read	read_url, parallel_read_url, capture_screenshot_url
utility	primer, show_api_key, expand_query, guess_datetime_url
rerank	sort_by_relevance, deduplicate_strings, deduplicate_images

Precedence

Filters are applied in this order (highest to lowest priority):

1. exclude_tools - Always excludes specified tools
2. exclude_tags - Excludes tools in specified tags
3. include_tools - Includes specified tools
4. include_tags - Starts with only tools in specified tags

Examples

Exclude parallel tools (saves ~4 tools worth of context tokens):

{
  "mcpServers": {
    "jina-mcp-server": {
      "url": "https://mcp.jina.ai/sse?exclude_tags=parallel",
      "headers": {
        "Authorization": "Bearer ${JINA_API_KEY}"
      }
    }
  }
}

Only include search and read tools:

{
  "mcpServers": {
    "jina-mcp-server": {
      "url": "https://mcp.jina.ai/sse?include_tags=search,read",
      "headers": {
        "Authorization": "Bearer ${JINA_API_KEY}"
      }
    }
  }
}

Exclude specific tools:

{
  "mcpServers": {
    "jina-mcp-server": {
      "url": "https://mcp.jina.ai/sse?exclude_tools=search_images,deduplicate_images",
      "headers": {
        "Authorization": "Bearer ${JINA_API_KEY}"
      }
    }
  }
}

Troubleshooting

I got stuck in a tool calling loop - what happened?

This is a common issue with LMStudio when the default context window is 4096 and you're using a thinking model like gpt-oss-120b or qwen3-4b-thinking. As the thinking and tool calling continue, once you hit the context window limit, the AI starts losing track of the beginning of the task. That's how it gets trapped in this rolling context window.

The solution is to load the model with enough context length to contain the full tool calling chain and thought process.

I can't see all tools.

Some MCP clients have local caching and do not actively update tool definitions. If you're not seeing all the available tools or if tools seem outdated, you may need to remove and re-add the jina-mcp-server to your MCP client configuration. This will force the client to refresh its cached tool definitions. In LMStudio, you can click the refresh button to load new tools.

Claude Desktop says "Server disconnected" on Windows

Cursor and Claude Desktop (Windows) have a bug where spaces inside args aren't escaped when it invokes npx, which ends up mangling these values. You can work around it using:

{
  // rest of config...
  "args": [
    "mcp-remote",
    "https://mcp.jina.ai/sse",
    "--header",
    "Authorization:${AUTH_HEADER}" // note no spaces around ':'
  ],
  "env": {
    "AUTH_HEADER": "Bearer <JINA_API_KEY>" // spaces OK in env vars
  }
},

Cursor shows a red dot on this MCP status

Likely a UI bug from Cursor, but the MCP works correctly without any problem. You can toggle off/on to "restart" the MCP if you find the red dot annoying (fact is, since you are using this as a remote MCP, it's not a real "server restart" but mostly a local proxy restart).

My LLM never uses some tools

Assuming all tools are enabled in your MCP client but LLM still never uses some tools or favors some over others, this is pretty common when an LLM is trained with a specific set of tools. For example, we rarely see parallel_* tools being used organically by LLMs unless they are explicitly instructed to do so. Some research says LLMs must be trained to use parallel_*. Models like Qwen3-Next natively prefer to call the singleton version but with multiple queries in an array to achieve parallelism (which our MCP also support now). Either way, in Cursor, you can add the following rule to your .mdc file:

---
alwaysApply: true
---

When you are uncertain about knowledge, or the user doubts your answer, always use Jina MCP tools to search and read best practices and latest information. Use search_arxiv and read_url together when questions relate to theoretical deep learning or algorithm details. Use search_ssrn for social sciences, economics, law, and finance research. search_web, search_arxiv, and search_ssrn cannot be used alone - always combine with read_url or parallel_read_url to read from multiple sources. Remember: every search must be complemented with read_url to read the source URL content. For maximum efficiency, use parallel_* versions of search and read when necessary.

Why is my content truncated?

Claude Code, Claude Desktop, and Cursor enforce a fixed 25k token limit on MCP tool responses. To prevent these clients from rejecting large responses entirely, this server applies a token guardrail specifically for read_url and parallel_read_url tools when connecting from these clients. For a single large content item, the text is truncated proportionally to fit within the token budget. For responses containing multiple items, the server keeps items in order until adding the next item would exceed the limit, then stops there. This ensures the response always fits within client constraints while preserving as much content as possible. Other clients like OpenAI Codex have configurable limits (tool_output_token_limit in config) so no server-side truncation is applied.

Using parallel tools vs singleton tools with arrays

Claude Code recently started preferring parallel_* tools (like parallel_search_web, parallel_read_url) for concurrent operations. However, models like Qwen3-Next prefer calling singleton tools with multiple queries in an array. Both approaches work: the singleton versions (search_web, search_arxiv, search_ssrn, read_url) accept either a single string or an array of strings for the query/url parameter. When given an array, these tools automatically execute all queries in parallel internally, producing the same concurrent behavior as explicitly calling parallel_* tools. Use whichever style your model prefers.

Alternative: Client-side tool filtering with mcp-remote

If you're using mcp-remote as a local proxy, you can also filter tools client-side using its --ignore-tool flag:

{
  "mcpServers": {
    "jina-mcp-server": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://mcp.jina.ai/sse",
        "--header",
        "Authorization: Bearer ${JINA_API_KEY}",
        "--ignore-tool", "parallel_search_web",
        "--ignore-tool", "parallel_search_arxiv",
        "--ignore-tool", "parallel_read_url"
      ]
    }
  }
}

This approach filters tools at the proxy level before they reach the MCP client. However, server-side filtering via query parameters (see Tool Filtering) is more efficient as it reduces token usage from the source.

Developer Guide

Local Development

# Clone the repository
git clone https://github.com/jina-ai/MCP.git
cd MCP

# Install dependencies
npm install

# Start development server
npm run start

Deploy to Cloudflare Workers

This will deploy your MCP server to a URL like: jina-mcp-server.<your-account>.workers.dev/sse