jina-reader-mcp
Enables fetching web pages via Jina Reader API, returning Markdown or JSON, with options for parallel fetches.
README
jina-reader-mcp
A minimal MCP server Docker image used to access a self-hosted or managed Jina Reader instance.
Usage
Starting the Server
docker run -d --rm \
--env JINA_API_KEY=<API key> \
--env JINA_BASE_URL=<URL> \
--end JINA_TIMEOUT=<timeout>
-p <port>:8000
ghcr.io/handcraftedbits/jina-reader-mcp:latest
Where:
<API key>is your Jina API key. Omit theJINA_API_KEYenvironment variable if the instance you're using doesn't require one.<URL>is the base URL of your Jina instance. If theJINA_BASE_URLenvironment variable is omitted,https://r.jina.aiwill be used.<timeout>is the default timeout in seconds for all operations. If theJINA_TIMEOUTenvironment variable is omitted, the default of30seconds will be used.<port>is the local port to map to. The MCP server will be available athttp://<host>:<port>.
Agent Details
Consult your agent harness's documentation for information on how to connect to the MCP server.
The MCP server will expose two tools:
web_fetch: Fetches a single page in either Markdown (default) or JSON formats. Parameters:content_type(optional, eitherjsonormarkdown): the format of the returned page. By default, this ismarkdown, and the tool description encourages agents to use this format.timeout(optional): the timeout, in seconds, for the fetch. By default, this is either30seconds or the value of theJINA_TIMEOUTenvironment variable.url(required): The URL of the page to fetch.with_all_images(optional): whether or not information about all the images on the page should be returned. By default, this isfalse.with_all_links(optional): whether or not information about all the links on the page should be returned. By default, this isfalse.
web_fetch_parallel: fetches up to five (maximum amount recommended by Jina) pages in parallel. Accepts a list of the same parameters asweb_fetch.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.