Skip to main content
Glama
andreasHornqvist

MCP Server Template for Cursor IDE

mcp_fetch

Fetches website content for analysis or integration by providing a URL. This tool retrieves web page data for use within development workflows.

Instructions

Fetches a website and returns its content

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL to fetch

Implementation Reference

  • Core handler function that asynchronously fetches the website using httpx, follows redirects, handles timeouts, HTTP errors, and exceptions, returning the HTML content or error as TextContent.
    async def fetch_website(
        url: str,
    ) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
        headers = {
            "User-Agent": "MCP Test Server (github.com/modelcontextprotocol/python-sdk)"
        }
        try:
            timeout = httpx.Timeout(10.0, connect=5.0)
            async with httpx.AsyncClient(
                follow_redirects=True, 
                headers=headers,
                timeout=timeout
            ) as client:
                response = await client.get(url)
                response.raise_for_status()
                return [types.TextContent(type="text", text=response.text)]
        except httpx.TimeoutException:
            return [types.TextContent(
                type="text",
                text="Error: Request timed out while trying to fetch the website."
            )]
        except httpx.HTTPStatusError as e:
            return [types.TextContent(
                type="text",
                text=(f"Error: HTTP {e.response.status_code} "
                      "error while fetching the website.")
            )]
        except Exception as e:
            return [types.TextContent(
                type="text",
                text=f"Error: Failed to fetch website: {str(e)}"
            )]
  • Dispatch logic in the call_tool handler that routes 'mcp_fetch' calls to the fetch_website function after validating the 'url' argument.
    if name == "mcp_fetch":
        if "url" not in arguments:
            return [types.TextContent(
                type="text",
                text="Error: Missing required argument 'url'"
            )]
        return await fetch_website(arguments["url"])
  • Input schema defining the required 'url' parameter as a string for the mcp_fetch tool.
        "type": "object",
        "required": ["url"],
        "properties": {
            "url": {
                "type": "string",
                "description": "URL to fetch",
            }
        },
    },
  • Tool registration in list_tools() that defines the name, description, and schema for 'mcp_fetch'.
    types.Tool(
        name="mcp_fetch",
        description="Fetches a website and returns its content",
        inputSchema={
            "type": "object",
            "required": ["url"],
            "properties": {
                "url": {
                    "type": "string",
                    "description": "URL to fetch",
                }
            },
        },
    ),
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 fetching and returning content but doesn't cover important traits like error handling, network timeouts, authentication needs, rate limits, or what 'content' entails (e.g., HTML, text). This leaves significant gaps for an agent.

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

Conciseness5/5

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

The description is a single, efficient sentence with zero waste. It's front-loaded and appropriately sized for a simple tool, making it easy to parse quickly.

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 (fetching websites can involve network issues, varied content types) and lack of annotations and output schema, the description is incomplete. It doesn't explain return values, error cases, or behavioral nuances, leaving the agent with insufficient information.

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% coverage with a clear description for the 'url' parameter. The description adds no additional meaning beyond the schema, such as URL format constraints or examples. Baseline 3 is appropriate when the schema does the heavy lifting.

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 verb ('fetches') and resource ('a website'), specifying what the tool does. However, it doesn't differentiate from potential siblings like 'figma_design' or 'generate_image', which are unrelated, so it lacks explicit sibling differentiation.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives or in what context it's appropriate. It simply states the action without any usage context, prerequisites, or exclusions.

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/andreasHornqvist/MCP'

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