ReviewWebsite MCP Server

convert_to_markdown

Convert a web page URL to Markdown using AI, producing LLM-friendly content for easy processing.

Instructions

Convert a URL to Markdown using AI via ReviewWeb.site API. Turn a web page into LLM-friendly content.

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": "convert_to_markdown"
  }
}

Input Schema

Name	Required	Description
`url`	Yes	The URL to convert to Markdown
`model`	No	AI model to use for conversion
`instructions`	No	Optional custom conversion guidance for the AI
`delayAfterLoad`	No	Optional delay after page load in milliseconds
`debug`	No	Enable debug mode for detailed logging
`api_key`	No	Your ReviewWebsite API key

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv1.3.1May 3, 2026

addedInput schema / properties / instructions

Added value: +{
+  "description": "Optional custom conversion guidance for the AI",
+  "type": "string"
+}

First observedv1.0.0Aug 18, 2025

TDQS

B3.3/5.0

Behavior2/5

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

No annotations are present, so the description carries the full burden. It mentions using an AI API but does not disclose behavioral details such as network requests, API key requirements, potential costs, rate limits, or output format beyond 'Markdown'.

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

Conciseness4/5

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

The description is only two sentences, clear and direct. The second sentence is slightly redundant with the first but does not significantly harm conciseness.

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 6 parameters, no output schema, and no annotations, the description is too brief. It does not explain return values, API key necessity, model options, or how instructions affect output, leaving significant gaps for effective tool use.

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?

With 100% schema description coverage, the baseline is 3. The description does not add extra meaning beyond the schema, such as hints about default model or the effect of instructions.

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 'Convert a URL to Markdown using AI via ReviewWeb.site API', specifying the verb 'convert', resource 'URL to Markdown', and distinguishing from sibling tools like convert_multiple_to_markdown (multiple URLs) and scrape_url (raw HTML).

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 implies usage for making web content LLM-friendly but lacks explicit when-to-use or when-not-to-use guidance. No comparison to alternatives like summarize_url or extract_data is provided.

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

Deploy Server