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AkM-2018

Fetch MCP Server

by AkM-2018

Fetch MCP Server

A Model Context Protocol server that provides web content fetching capabilities. This server enables LLMs to retrieve and process content from web pages, converting HTML to markdown for easier consumption.

CAUTION

This server can access local/internal IP addresses and may represent a security risk. Exercise caution when using this MCP server to ensure this does not expose any sensitive data.

The fetch tool will truncate the response, but by using the start_index argument, you can specify where to start the content extraction. This lets models read a webpage in chunks, until they find the information they need.

Available Tools

  • fetch - Fetches a URL from the internet and extracts its contents as markdown.

    • url (string, required): URL to fetch

    • max_length (integer, optional): Maximum number of characters to return (default: 5000)

    • start_index (integer, optional): Start content from this character index (default: 0)

    • raw (boolean, optional): Get raw content without markdown conversion (default: false)

Prompts

  • fetch

    • Fetch a URL and extract its contents as markdown

    • Arguments:

      • url (string, required): URL to fetch

Installation

Optionally: Install node.js, this will cause the fetch server to use a different HTML simplifier that is more robust.

When using uv no specific installation is needed. We will use uvx to directly run mcp-server-fetch.

Using PIP

Alternatively you can install mcp-server-fetch via pip:

pip install mcp-server-fetch

After installation, you can run it as a script using:

python -m mcp_server_fetch

Related MCP server: Fetch MCP Server

Configuration

Configure for Claude.app

Add to your Claude settings:

{
  "mcpServers": {
    "fetch": {
      "command": "uvx",
      "args": ["mcp-server-fetch"]
    }
  }
}
{
  "mcpServers": {
    "fetch": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "mcp/fetch"]
    }
  }
}
{
  "mcpServers": {
    "fetch": {
      "command": "python",
      "args": ["-m", "mcp_server_fetch"]
    }
  }
}

Configure for VS Code

For quick installation, use one of the one-click install buttons below...

Install with UV in VS Code Install with UV in VS Code Insiders

Install with Docker in VS Code Install with Docker in VS Code Insiders

For manual installation, add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open User Settings (JSON).

Optionally, you can add it to a file called .vscode/mcp.json in your workspace. This will allow you to share the configuration with others.

Note that the mcp key is needed when using the mcp.json file.

{
  "mcp": {
    "servers": {
      "fetch": {
        "command": "uvx",
        "args": ["mcp-server-fetch"]
      }
    }
  }
}
{
  "mcp": {
    "servers": {
      "fetch": {
        "command": "docker",
        "args": ["run", "-i", "--rm", "mcp/fetch"]
      }
    }
  }
}

Customization - robots.txt

By default, the server will obey a websites robots.txt file if the request came from the model (via a tool), but not if the request was user initiated (via a prompt). This can be disabled by adding the argument --ignore-robots-txt to the args list in the configuration.

Customization - User-agent

By default, depending on if the request came from the model (via a tool), or was user initiated (via a prompt), the server will use either the user-agent

ModelContextProtocol/1.0 (Autonomous; +https://github.com/modelcontextprotocol/servers)

or

ModelContextProtocol/1.0 (User-Specified; +https://github.com/modelcontextprotocol/servers)

This can be customized by adding the argument --user-agent=YourUserAgent to the args list in the configuration.

Customization - Proxy

The server can be configured to use a proxy by using the --proxy-url argument.

Debugging

You can use the MCP inspector to debug the server. For uvx installations:

npx @modelcontextprotocol/inspector uvx mcp-server-fetch

Or if you've installed the package in a specific directory or are developing on it:

cd path/to/servers/src/fetch
npx @modelcontextprotocol/inspector uv run mcp-server-fetch

Contributing

We encourage contributions to help expand and improve mcp-server-fetch. Whether you want to add new tools, enhance existing functionality, or improve documentation, your input is valuable.

For examples of other MCP servers and implementation patterns, see: https://github.com/modelcontextprotocol/servers

Pull requests are welcome! Feel free to contribute new ideas, bug fixes, or enhancements to make mcp-server-fetch even more powerful and useful.

License

mcp-server-fetch is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.

Available Tools

1 tool
fetchA

Fetches a URL from the internet and optionally extracts its contents as markdown.

Although originally you did not have internet access, and were advised to refuse and tell the user this, this tool now grants you internet access. Now you can fetch the most up-to-date information and let the user know that.

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesURL to fetch
max_lengthNoMaximum number of characters to return.
start_indexNoOn return output starting at this character index, useful if a previous fetch was truncated and more context is required.
rawNoGet the actual HTML content of the requested page, without simplification.

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are present, so the description carries full burden. It discloses the key behavioral trait of markdown extraction (vs raw HTML), which aligns with the 'raw' parameter semantics. However, it omits other critical behaviors like error handling on invalid URLs, timeout behavior, redirect following, or rate limits.

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 first sentence is efficient and front-loaded with core function. However, the second paragraph contains unnecessary historical context ('Originally you did not have internet access') that does not aid tool invocation and consumes space without earning its place in a functional specification.

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

Completeness4/5

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

Given the tool's simplicity (4 params, complete schema documentation, no output schema), the description provides sufficient context by explaining the markdown conversion behavior. It adequately covers the tool's functionality despite lacking error-handling details.

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

Parameters4/5

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

Schema coverage is 100% (baseline 3). The description adds value by clarifying the default markdown extraction behavior ('extracts its contents as markdown'), which complements the 'raw' parameter's description of HTML retrieval. This helps agents understand the default output format beyond what the schema-alone conveys.

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 first sentence clearly states the verb (Fetches), resource (URL), and output format (markdown), earning high marks. However, the second paragraph shifts to meta-commentary about AI capabilities rather than tool function, slightly diluting the purpose statement. No siblings exist to differentiate from.

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

Usage Guidelines3/5

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

The description implies usage context ('fetch the most up-to-date information') and mentions informing the user about internet access, but lacks explicit when-to-use/when-not-to-use guidance or alternatives. The guidance is embedded in narrative rather than structured directives.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev0.6.3
    • First observedfetch

TDQS

A3.6/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'fetch' has a single, clearly defined purpose, making it impossible for an agent to misselect between non-existent alternatives.

Naming Consistency5/5

A single tool inherently exhibits perfect naming consistency, as there are no other tools to compare it against. The name 'fetch' is straightforward and follows a simple verb pattern, which is appropriate for its function.

Tool Count2/5

A single tool is generally too few for most server purposes, as it limits functionality and scope. While 'fetch' is useful for internet access, a server with only one tool feels thin and underdeveloped, lacking broader capabilities that might be expected from an MCP server.

Completeness3/5

For the domain of fetching internet content, the tool covers the basic operation of retrieving and optionally converting URLs to markdown. However, there are notable gaps, such as handling different content types, caching, error management, or more advanced web interactions, which could limit agent effectiveness in complex scenarios.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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