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Fetch-Save MCP Server

by gregberns

fetch-save

Download and save web content to a local file for permanent storage and future access, enabling easy retrieval and processing of online data.

Instructions

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.

Input Schema

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

Implementation Reference

  • The main handler function for the 'fetch-save' tool, invoked via @server.call_tool(). It parses arguments using the Fetch schema, checks robots.txt if not ignored, fetches the URL content using fetch_url, saves it to the specified filepath using save_content_to_file, and returns a success message.
    @server.call_tool()
    async def call_tool(name, arguments: dict) -> list[TextContent]:
        try:
            args = Fetch(**arguments)
        except ValueError as e:
            raise McpError(ErrorData(code=INVALID_PARAMS, message=str(e)))
    
        url = str(args.url)
        filepath = str(args.filepath)
    
        if not url:
            raise McpError(ErrorData(code=INVALID_PARAMS, message="URL is required"))
    
        if not filepath:
            raise McpError(ErrorData(code=INVALID_PARAMS, message="Filepath is required"))
    
        if not ignore_robots_txt:
            await check_may_autonomously_fetch_url(url, user_agent_autonomous, proxy_url)
    
        content, prefix, content_type = await fetch_url(
            url, user_agent_autonomous, force_raw=False, proxy_url=proxy_url
        )
        original_length = len(content)
    
        save_content_to_file(content, filepath)
        
        return [TextContent(type="text", text=f"{prefix}Successfully DOWNLOADED and SAVED content from {url} to {filepath}. The file has been created for permanent storage. Content length: {len(content)} characters.\n")]
  • Pydantic model defining the input schema for the 'fetch-save' tool, with 'url' and 'filepath' fields.
    class Fetch(BaseModel):
        """Parameters for fetching a URL and saving the content to a local file for storage and future use."""
    
        url: Annotated[AnyUrl, Field(description="URL to fetch and download for local storage")]
        filepath: Annotated[FilePath, Field(description="Local filepath where the downloaded content will be saved")]
  • Tool registration in list_tools(), defining the 'fetch-save' tool with its name, description, and input schema.
        @server.list_tools()
        async def list_tools() -> list[Tool]:
            return [
                Tool(
                    name="fetch-save",
                    description="""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.""",
                    inputSchema=Fetch.model_json_schema(),
                )
            ]
  • Helper function to fetch URL content using httpx, simplify HTML to markdown if possible, and return content, prefix, and content_type. Used by the handler.
    async def fetch_url(
        url: str, user_agent: str, force_raw: bool = False, proxy_url: str | None = None
    ) -> Tuple[str, str, str]:
        """
        Fetch the URL and return the content in a form ready for the LLM, a prefix string with status information, and the content type.
        """
        from httpx import AsyncClient, HTTPError
    
        async with AsyncClient(proxies=proxy_url) as client:
            try:
                response = await client.get(
                    url,
                    follow_redirects=True,
                    headers={"User-Agent": user_agent},
                    timeout=30,
                )
            except HTTPError as e:
                raise McpError(ErrorData(code=INTERNAL_ERROR, message=f"Failed to fetch {url}: {e!r}"))
            if response.status_code >= 400:
                raise McpError(ErrorData(
                    code=INTERNAL_ERROR,
                    message=f"Failed to fetch {url} - status code {response.status_code}",
                ))
    
            page_raw = response.text
    
        content_type = response.headers.get("content-type", "")
        is_page_html = (
            "<html" in page_raw[:100] or "text/html" in content_type or not content_type
        )
    
        if is_page_html and not force_raw:
            return extract_content_from_html(page_raw), "", content_type
    
        return (
            page_raw,
            f"Content type {content_type} cannot be simplified to markdown, but here is the raw content:\n",
            content_type
        )
  • Helper function to save the fetched content to the specified local filepath, creating directories if needed. Used by the handler.
    def save_content_to_file(content: str, filepath: str) -> None:
        """Save content to a file.
    
        Args:
            content: Content to save
            filepath: Path to the file where content should be saved
    
        Raises:
            OSError: If there is an error saving to the file
        """
        # Create directory if it doesn't exist
        directory = os.path.dirname(filepath)
        if directory and not os.path.exists(directory):
            os.makedirs(directory)
    
        # Write content to file
        with open(filepath, 'w', encoding='utf-8') as f:
            f.write(content)
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.

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