Agent Data API

web_contents

Read up to 5 web pages in one call as clean text for an LLM, in the Exa /contents shape: POST {urls: [...], text: {maxCharacters}} and get results with url, title, author, publishedDate, image and the main text as Markdown (navigation, header, footer and scripts dropped), plus a per-URL status. One flat price per call. Costs $0.002 USDC on Base via x402.

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": {
      "urls": "<urls>"
    },
    "name": "web_contents"
  }
}

Input Schema

Name	Required	Description	Default
`text`	No	Optional {maxCharacters} per page (default 20,000)
`urls`	Yes	1 to 5 page URLs

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 10, 2026

TDQS

A4.4/5.0

Behavior5/5

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

This definition is highly transparent, disclosing complex behaviors like stripping navigation, headers, footers, and scripts. It also provides critical operational information regarding cost and the specific output structure of the content.

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 dense but extremely efficient, packing high-value information (e.g., 'Exa/contents shape', '5 web pages', 'scripts dropped', 'flat price') into a concise paragraph without unnecessary filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there is no output schema, the description is highly complete by detailing the specific metadata and main text fields returned, as well as the total page limit and pricing.

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 coverage is 100%, meaning the schema captures all parameter details. The description adds no further functional meaning to how the provided parameters operate.

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 tool clearly states it reads up to 5 web pages and outputs clean text tailored for an LLM, specifying its output format and bulk nature, which differentiates it from generic web fetching tools.

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 strongly implies when to use the tool: specifically when the resulting data must be LLM-ready and requires metadata extraction. It guides the user toward this specialized use case with its format details.

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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