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mcp-jina-ai

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Jina AI MCP Server

An MCP server that provides access to Jina AI's powerful web services through Claude. This server implements three main tools:

  • Web page reading and content extraction

  • Web search

  • Fact checking/grounding

Features

Tools

read_webpage

  • Extract content from web pages in a format optimized for LLMs

  • Supports multiple output formats (Default, Markdown, HTML, Text, Screenshot, Pageshot)

  • Options for including links and images

  • Ability to generate alt text for images

  • Cache control options

search_web

  • Search the web using Jina AI's search API

  • Configurable number of results (default: 5)

  • Support for image retention and alt text generation

  • Multiple return formats (markdown, text, html)

  • Returns structured results with titles, descriptions, and content

fact_check

  • Fact-check statements using Jina AI's grounding engine

  • Provides factuality scores and supporting evidence

  • Optional deep-dive mode for more thorough analysis

  • Returns references with key quotes and supportive/contradictory classification

Related MCP server: sysauto Ask MCP Server

Setup

Prerequisites

You'll need a Jina AI API key to use this server. Get one for free at https://jina.ai/

Installation

There are two ways to use this server:

Installing via Smithery

To install Jina AI for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install jina-ai-mcp-server --client claude

Option 1: NPX (Recommended)

Add this configuration to your Claude Desktop config file:

{
  "mcpServers": {
    "jina-ai-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "jina-ai-mcp-server"
      ],
      "env": {
        "JINA_API_KEY": "<YOUR_KEY>"
      }
    }
  }
}

Option 2: Local Installation

  1. Clone the repository

  2. Install dependencies:

npm install

  1. Build the server:

npm run build

  1. Add this configuration to your Claude Desktop config:

{
  "mcpServers": {
    "jina-ai-mcp-server": {
      "command": "node",
      "args": [
        "/path/to/jina-ai-mcp-server/dist/index.js"
      ],
      "env": {
        "JINA_API_KEY": "<YOUR_KEY>"
      }
    }
  }
}

Config File Location

On MacOS:

~/Library/Application Support/Claude/claude_desktop_config.json

On Windows:

%APPDATA%/Claude/claude_desktop_config.json

Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector:

npm run inspector

The Inspector will provide a URL to access debugging tools in your browser.

API Response Types

All tools return structured JSON responses that include:

  • Status codes and metadata

  • Formatted content based on the requested output type

  • Usage information (token counts)

  • When applicable: images, links, and additional metadata

For detailed schema information, see schemas.ts.

Running evals

The evals package loads an mcp client that then runs the index.ts file, so there is no need to rebuild between tests. You can load environment variables by prefixing the npx command. Full documentation can be found here.

OPENAI_API_KEY=your-key  npx mcp-eval evals.ts index.ts

Available Tools

3 tools
fact_checkC

Fact-check a statement using Jina AI's grounding engine

ParametersJSON Schema
NameRequiredDescriptionDefault
deepdiveNo
statementYes

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden but only states the action. It does not disclose whether the tool returns a verdict, an explanation, or requires additional context. No mention of side effects or limitations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise, single sentence, front-loaded with the main purpose. However, it sacrifices informative details that could be added without much length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, no annotations, and 2 parameters, the description lacks details on return values, error cases, and parameter behavior. It is insufficient for an agent to fully understand the tool's usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, and the description adds no meaning to parameters. The 'deepdive' boolean parameter is not explained. The description only mentions 'statement' implicitly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'fact-check', the resource 'a statement', and the specific engine 'Jina AI's grounding engine'. It distinguishes itself from sibling tools 'read_webpage' and 'search_web' by focusing on verification rather than retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. For instance, it does not clarify that fact-check should be used for verifying claims while search_web or read_webpage are for general information gathering.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

read_webpageC

Extract content from a webpage in a format optimized for LLMs

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYes
formatNo
no_cacheNo
with_linksNo
with_imagesNo
with_generated_altNo

TDQS

C2.6/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided. Description lacks disclosure of caching, rate limits, error behavior, or format implications. 'Optimized for LLMs' is vague.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Very concise single sentence but at cost of completeness. Front-loads purpose but does not earn its place with meaningful detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 6 parameters, no output schema, and no annotations, the description is severely incomplete. Does not cover return values, parameter details, or behavioral aspects.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%. Description does not explain any of the 6 parameters (e.g., format, no_cache, with_links). Fails to compensate for missing schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states verb 'extract', resource 'content from a webpage', and purpose 'optimized for LLMs'. It distinguishes from siblings like 'fact_check' and 'search_web'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use vs siblings or when not to use. Lacks context for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_webC

Search the web using Jina AI's search API

ParametersJSON Schema
NameRequiredDescriptionDefault
countNo
queryYes
retain_imagesNonone
return_formatNomarkdown
with_generated_altNo

TDQS

C2.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must carry the full burden. It only states 'search the web' without disclosing behavioral traits like rate limits, result count limits, or idempotency. The minimal info does not cover core behavioral expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, which is concise but overly minimal. It lacks structure and does not effectively organize details; brevity here sacrifices completeness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 5 parameters, no output schema, no annotations, and sibling tools, the description is incomplete. It fails to explain return format, parameter effects, or differentiate from related tools, leaving significant gaps for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 5 parameters with 0% description coverage. The description adds no meaning beyond the schema fields. Parameters like 'count', 'retain_images', and 'return_format' are left unexplained, forcing the agent to guess their semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches the web using Jina AI's API, identifying the verb and resource. However, it does not differentiate from sibling tools like fact_check or read_webpage, missing an opportunity to clarify scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description lacks explicit context or exclusions, leaving the agent to infer usage independently.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.3/5.0
Disambiguation5/5

Each tool targets a distinct operation: fact-checking a statement, reading a webpage, and searching the web. No overlap in purpose.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using snake_case: fact_check, read_webpage, search_web.

Tool Count5/5

With three tools, the set is well-scoped for a web and grounding server, covering the core tasks without extraneous tools.

Completeness5/5

The tool surface covers the essential workflow: search, retrieve, and verify. No obvious gaps given the server's apparent purpose.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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