Skip to main content
Glama
aglolz

Fetch MCP Server

by aglolz

fetch

Retrieve web content from URLs and convert HTML to markdown format. Access up-to-date information from public websites and local networks with configurable output options.

Instructions

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.

Input Schema

TableJSON 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.

Implementation Reference

  • Handler for the 'fetch' tool: validates arguments with Fetch schema, checks robots.txt compliance, fetches URL content (HTML to markdown), handles truncation and continuation prompts.
    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)
        if not url:
            raise McpError(ErrorData(code=INVALID_PARAMS, message="URL is required"))
    
        if not ignore_robots_txt:
            await check_may_autonomously_fetch_url(url, user_agent_autonomous, proxy_url)
    
        content, prefix = await fetch_url(
            url, user_agent_autonomous, force_raw=args.raw, proxy_url=proxy_url
        )
        original_length = len(content)
        if args.start_index >= original_length:
            content = "<error>No more content available.</error>"
        else:
            truncated_content = content[args.start_index : args.start_index + args.max_length]
            if not truncated_content:
                content = "<error>No more content available.</error>"
            else:
                content = truncated_content
                actual_content_length = len(truncated_content)
                remaining_content = original_length - (args.start_index + actual_content_length)
                # Only add the prompt to continue fetching if there is still remaining content
                if actual_content_length == args.max_length and remaining_content > 0:
                    next_start = args.start_index + actual_content_length
                    content += f"\n\n<error>Content truncated. Call the fetch tool with a start_index of {next_start} to get more content.</error>"
        return [TextContent(type="text", text=f"{prefix}Contents of {url}:\n{content}")]
  • Pydantic BaseModel defining the input schema for the 'fetch' tool, including URL, max_length, start_index for continuation, and raw flag.
    class Fetch(BaseModel):
        """Parameters for fetching a URL."""
    
        url: Annotated[AnyUrl, Field(description="URL to fetch")]
        max_length: Annotated[
            int,
            Field(
                default=5000,
                description="Maximum number of characters to return.",
                gt=0,
                lt=1000000,
            ),
        ]
        start_index: Annotated[
            int,
            Field(
                default=0,
                description="On return output starting at this character index, useful if a previous fetch was truncated and more context is required.",
                ge=0,
            ),
        ]
        raw: Annotated[
            bool,
            Field(
                default=False,
                description="Get the actual HTML content of the requested page, without simplification.",
            ),
        ]
  • Registers the 'fetch' tool with MCP Server using list_tools decorator, providing name, description, and input schema from Fetch model.
        @server.list_tools()
        async def list_tools() -> list[Tool]:
            return [
                Tool(
                    name="fetch",
                    description="""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.""",
                    inputSchema=Fetch.model_json_schema(),
                )
            ]
  • Core helper function that performs the HTTP GET request to fetch URL content, detects HTML and extracts to markdown using readabilipy and markdownify, or returns raw with prefix.
    async def fetch_url(
        url: str, user_agent: str, force_raw: bool = False, proxy_url: str | None = None
    ) -> Tuple[str, str]:
        """
        Fetch the URL and return the content in a form ready for the LLM, as well as a prefix string with status information.
        """
        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), ""
    
        return (
            page_raw,
            f"Content type {content_type} cannot be simplified to markdown, but here is the raw content:\n",
        )
  • Helper function to check robots.txt compliance using Protego parser before allowing autonomous fetch, raises McpError if disallowed.
    async def check_may_autonomously_fetch_url(url: str, user_agent: str, proxy_url: str | None = None) -> None:
        """
        Check if the URL can be fetched by the user agent according to the robots.txt file.
        Raises a McpError if not.
        """
        from httpx import AsyncClient, HTTPError
    
        robot_txt_url = get_robots_txt_url(url)
    
        async with AsyncClient(proxies=proxy_url) as client:
            try:
                response = await client.get(
                    robot_txt_url,
                    follow_redirects=True,
                    headers={"User-Agent": user_agent},
                )
            except HTTPError:
                raise McpError(ErrorData(
                    code=INTERNAL_ERROR,
                    message=f"Failed to fetch robots.txt {robot_txt_url} due to a connection issue",
                ))
            if response.status_code in (401, 403):
                raise McpError(ErrorData(
                    code=INTERNAL_ERROR,
                    message=f"When fetching robots.txt ({robot_txt_url}), received status {response.status_code} so assuming that autonomous fetching is not allowed, the user can try manually fetching by using the fetch prompt",
                ))
            elif 400 <= response.status_code < 500:
                return
            robot_txt = response.text
        processed_robot_txt = "\n".join(
            line for line in robot_txt.splitlines() if not line.strip().startswith("#")
        )
        robot_parser = Protego.parse(processed_robot_txt)
        if not robot_parser.can_fetch(str(url), user_agent):
            raise McpError(ErrorData(
                code=INTERNAL_ERROR,
                message=f"The sites robots.txt ({robot_txt_url}), specifies that autonomous fetching of this page is not allowed, "
                f"<useragent>{user_agent}</useragent>\n"
                f"<url>{url}</url>"
                f"<robots>\n{robot_txt}\n</robots>\n"
                f"The assistant must let the user know that it failed to view the page. The assistant may provide further guidance based on the above information.\n"
                f"The assistant can tell the user that they can try manually fetching the page by using the fetch prompt within their UI.",
            ))
Behavior2/5

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 mentions internet access and optional markdown extraction, but fails to detail critical behaviors such as error handling (e.g., for invalid URLs), rate limits, authentication needs, or what happens when max_length is exceeded. This leaves significant gaps in understanding how the tool operates.

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

Conciseness2/5

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

The description is not front-loaded efficiently; the first sentence is clear, but the second sentence adds redundant historical context about internet access that doesn't aid tool selection or invocation. This extra information reduces conciseness without adding practical value for the agent.

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 the tool's complexity (internet fetching with multiple parameters) and lack of annotations and output schema, the description is incomplete. It doesn't explain return values, error conditions, or behavioral nuances, leaving the agent with insufficient information to use the tool effectively in varied contexts.

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 details for all four parameters (url, max_length, start_index, raw). The description adds minimal value beyond this, only implying that 'extracts its contents as markdown' relates to the raw parameter. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.

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's purpose: 'Fetches a URL from the internet and optionally extracts its contents as markdown.' It specifies the verb ('fetches') and resource ('URL'), and distinguishes between fetching and optional markdown extraction. However, since there are no sibling tools, the differentiation aspect is not applicable, preventing a perfect score.

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 provides implied usage guidance by explaining that the tool grants internet access, which was previously unavailable, and suggests using it for up-to-date information. However, it lacks explicit instructions on when to use this tool versus alternatives (e.g., other data retrieval methods) or any exclusions, making it somewhat vague.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/aglolz/mcp-fetch'

If you have feedback or need assistance with the MCP directory API, please join our Discord server