AgentRender

extract

Retrieve title, meta description, canonical, main text, and links from any URL. Provides structured page data for AI agents.

Instructions

Extract title, meta description, canonical, main text, and links from a URL (non-LLM).

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": "extract"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes
`include_links`	No
`max_text_chars`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.1Sep 22, 2026

TDQS

B3.3/5.0

Behavior3/5

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

With no annotations, the description carries the burden, and it does disclose the core behavior: a non-LLM deterministic extraction of the listed page fields. It does not mention failure behavior, redirects, or dynamic-content limitations, which would be useful for a network-fetching tool. The disclosed scope is accurate and non-contradictory.

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?

A single sentence front-loads the action and the output list, with no filler and no repetition of the tool name. The parenthetical adds a useful qualifier without waste.

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?

Although an output schema exists and can describe return values, the definition omits the meaning of two optional parameters and any guidance on selecting between extract and its pdf/screenshot siblings. For a 3-parameter tool with no annotations, this leaves important ordering and invocation decisions underspecified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, but it only covers the URL semantically via 'from a URL'. It never explains include_links or max_text_chars, leaving the agent to infer their effects and defaults. The word 'links' hints at include_links behavior but does not explicitly connect them.

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 opens with the verb 'Extract' and enumerates exact resources: title, meta description, canonical, main text, and links from a URL. This is specific enough to distinguish it from the pdf and screenshot siblings, which capture rendered representations rather than semantic content. The parenthetical '(non-LLM)' further clarifies the extraction method.

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 gives no explicit when-to-use guidance or exclusions. The sibling tools pdf and screenshot are known but never compared, so an agent cannot tell whether to choose extract versus a rendering tool for a URL. The only hint is '(non-LLM)', which describes method rather than usage conditions.

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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