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markfetch-mcp

markfetch-mcp

by asdfas988
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

scrape_url

Fetch any web page and convert its main content into clean, LLM-ready markdown for model context.

Instructions

Fetch a web page and return its main content as clean, LLM-ready markdown. Use this to give the model the content of a URL.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>"
    },
    "name": "scrape_url"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYesThe web page URL to read

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations provided, the description carries the burden of disclosing behavior. It does mention that the tool returns 'main content' and 'clean, LLM-ready markdown,' which gives insight into its extraction and formatting behavior. However, it does not disclose potential limitations (e.g., JavaScript-rendered pages, access restrictions, or handling of non-HTML content), leaving some behavioral traits unspecified.

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 extremely concise, containing two short sentences that front-load the primary function and output format. There is no redundant phrasing or unnecessary detail; every word contributes to understanding.

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?

Given the tool's low complexity (one required parameter, no output schema, no annotations), the description is nearly complete. It covers what the tool does, what it returns, and when to use it. The only gap is lack of explicit differentiation from the sibling screenshot_url, but for a simple, single-purpose tool, the description provides sufficient context.

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 already provides a complete description for the single parameter 'url' ('The web page URL to read'), achieving 100% coverage. The tool description adds no additional semantics for this parameter, so the baseline score of 3 is warranted.

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 uses specific language: 'Fetch a web page and return its main content as clean, LLM-ready markdown.' This clearly states the verb (fetch), resource (web page), and output (main content as markdown), distinguishing it from the sibling screenshot_url which captures visual images rather than textual content.

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

Usage Guidelines4/5

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

The phrase 'Use this to give the model the content of a URL' provides explicit usage context, indicating when the tool should be invoked. However, it does not mention alternatives or exclusions (e.g., 'for visual layouts use screenshot_url'), so it lacks full contrast. Since usage is explicitly called out, a 4 is appropriate.

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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