Fetch-Save MCP Server
The Fetch-Save MCP Server allows you to fetch web content and save it locally for future use.
Key capabilities:
Fetch and Save Web Content: Download content from URLs and store it permanently in local files
HTML to Markdown Conversion: Convert fetched HTML content into more readable markdown format
Customization Options: Configure custom user-agent strings, proxy routing, and robots.txt handling
Integration Support: Easily integrate with applications like Claude and VS Code
Debugging Tools: Use the MCP inspector for troubleshooting
Extensibility: Customize or contribute to add new features
Converts fetched HTML content to Markdown format for better readability and processing by language models.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Fetch-Save MCP Serverfetch https://example.com/blog and save to /docs/example_blog.md"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
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 downloadfilepath(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 downloadfilepath(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.
Using uv (recommended)
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-saveAfter installation, you can run it as a script using:
python -m mcp_server_fetch_saveConfiguration
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
mcpkey is needed when using themcp.jsonfile.
{
"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-saveOr 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-saveContributing
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 toolfetch-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.
| Name | Required | Description | Default |
|---|---|---|---|
| filepath | Yes | Local filepath where the downloaded content will be saved | |
| url | Yes | URL to fetch and download for local storage |
TDQS
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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