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Fetch-Save MCP Server

by gregberns

Fetch-Save MCP Server

A Model Context Protocol server that provides web content fetching and local file saving capabilities. This server enables LLMs to retrieve content from web pages, convert HTML to markdown for easier consumption, and save the retrieved content to a local file.

The key difference from the standard fetch MCP server is that this server provides a fetch-save tool that both retrieves content AND stores it locally in a permanent file, allowing for later access or processing of the data.

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.

Additional Note: The Readme and some code was written/edited with Claude Code - so parts may be incorrect. Please submit a PR if there are changes needed.

Available Tools

  • fetch-save - Fetches a URL from the internet, extracts its contents as markdown, and SAVES it to a local file.

    • url (string, required): URL to fetch and download

    • filepath (string, required): Local filepath where the downloaded content will be saved

Related MCP server: MCP URL Fetcher

Prompts

  • fetch-save

    • Fetch a URL and save its contents to a local file

    • Arguments:

      • url (string, required): URL to fetch and download

      • filepath (string, required): Local filepath where content will be saved

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-save.

Using PIP

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

pip install mcp-server-fetch-save

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

python -m mcp_server_fetch_save

Configuration

Configure for Claude.app

Add to your Claude settings:

"mcpServers": {
  "fetch-save": {
    "command": "uvx",
    "args": ["mcp-server-fetch-save"]
  }
}
"mcpServers": {
  "fetch-save": {
    "command": "python",
    "args": ["-m", "mcp_server_fetch_save"]
  }
}

Configure for VS Code

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-save": {
        "command": "uvx",
        "args": ["mcp-server-fetch-save"]
      }
    }
  }
}

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 download this repo, and add this to your .mcp.json file to run/test locallly.

{
  "mcpServers": {
    "fetch_save": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/clone/of/project/mcp-server-fetch-save/src/mcp_server_fetch_save",
        "run",
        "__main__.py"
      ]
    }
  }
}

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

npx @modelcontextprotocol/inspector uvx mcp-server-fetch-save

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

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

Contributing

We encourage contributions to help expand and improve mcp-server-fetch-save. 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-save even more powerful and useful.

License

mcp-server-fetch-save 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.

Thanks

This server was developed based on the original modelcontextprotocol/servers fetch server, with additional functionality for saving content to local files.

Available Tools

1 tool
fetch-saveA

Fetches a URL from the internet and SAVES the contents to a LOCAL FILE. This tool is specifically designed for DOWNLOADING and STORING web content to your filesystem.

When you need to both access online content AND save it locally for later use or processing, THIS is the appropriate tool to use. Unlike the regular fetch tool which only displays content, this tool permanently stores the fetched data in a file.

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
filepathYesLocal filepath where the downloaded content will be saved
urlYesURL to fetch and download for local storage

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: that the tool performs a download operation, permanently stores data to the filesystem, grants internet access capability, and fetches up-to-date information. However, it doesn't mention potential limitations like file size constraints, network timeouts, or error handling scenarios.

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 appropriately front-loaded with the core functionality, but contains some redundant phrasing and historical context about internet access that could be more concise. The third paragraph about previously lacking internet access adds context but could be integrated more efficiently into the usage guidelines.

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?

For a tool with 2 parameters, 100% schema coverage, and no annotations or output schema, the description provides good contextual completeness. It explains the tool's purpose, usage context, behavioral characteristics, and internet access capability. The main gap is the lack of information about return values or error conditions, which would be helpful given there's no output schema.

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

Parameters3/5

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

The input schema has 100% description coverage, providing clear documentation for both parameters. The description adds some context by mentioning 'downloading and storing web content' and 'local file for storage and future use,' but doesn't provide additional semantic details beyond what's already in the schema descriptions. This meets the baseline expectation when schema coverage is complete.

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 tool's purpose with specific verbs ('fetches', 'saves', 'downloading', 'storing') and resources ('URL', 'web content', 'local file', 'filesystem'). It explicitly distinguishes this from a hypothetical 'regular fetch tool' that only displays content, establishing clear differentiation even without actual sibling tools.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool: 'When you need to both access online content AND save it locally for later use or processing, THIS is the appropriate tool to use.' It also clearly contrasts with an alternative ('regular fetch tool which only displays content') and specifies the tool's internet access capability that overrides previous limitations.

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

TDQS

A4/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool's purpose is clearly defined as fetching and saving web content, making it distinct by default.

Naming Consistency5/5

The single tool name 'fetch-save' follows a consistent verb-verb pattern that clearly describes its dual functionality. There are no other tools to compare against, so consistency is inherently perfect.

Tool Count2/5

A single tool is too few for a server named 'Fetch-Save MCP Server', which implies a broader scope of operations. While the tool itself is useful, the server lacks complementary tools like list, delete, or manage saved files, making it feel incomplete and thin for its apparent purpose.

Completeness2/5

The server is severely incomplete for a fetch-and-save domain. It only provides a download-and-store operation, with no tools for managing saved files (e.g., list, read, delete, update) or handling errors, which will limit agent workflows and cause dead ends in tasks.

Maintenance

ActivityInactive
ResponsivenessNo issues

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