web2md-mcp

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fetch

Convert any web page URL into clean Markdown for LLM or RAG pipelines. Retrieves the main article content, stripping clutter, using the web2md service.

Instructions

Fetch a web page URL and return its main content as clean Markdown.

Uses the web2md service (Crawl4AI) behind the scenes. Best for feeding article / documentation text into an LLM or RAG pipeline.

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": "fetch"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes	target web page URL to fetch and convert.
`quality`	No	'fast' = Crawl4AI raw Markdown (complete body text).	fast
`anti_bot`	No	crawling strategy — 'auto' (default, undetected+stealth), 'stealth', 'undetected', or 'proxy'.	auto

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 6, 2026

TDQS

A4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool uses the web2md service (Crawl4AI) and returns main content as clean Markdown. However, it does not cover failure modes, dynamic rendering, rate limits, or other behavioral traits that might affect usage.

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 two sentences long. The first sentence front-loads the action and result, and the second adds useful context about the backend and use case. Every word earns its place.

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?

The tool has a straightforward purpose, an output schema, and well-documented parameters. The description covers the main purpose, use case, and underlying service. Minor gaps exist around error handling and edge cases, but these are not critical given the schema depth.

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?

Schema description coverage is 100%, with each parameter (url, quality, anti_bot) individually described. The description adds little beyond the schema—only clarifying the output format (Markdown). Since the schema already 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.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool fetches a web page URL and returns its main content as clean Markdown. This is a specific verb+resource+outcome combination. Although there are no sibling tools to distinguish from, the purpose is unambiguous and actionable.

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 description explicitly names the ideal use case: 'feeding article / documentation text into an LLM or RAG pipeline.' This provides clear context for when to use the tool. However, it does not mention exclusions or alternatives, which would be necessary for a 5.

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

Deploy Server

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