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Extract from web pages

extract_web_page
Read-only

Read up to five public pages at once and get back the passages that answer an objective, e.g. 'the price of each plan' or 'who is on the leadership team'. Faster and cheaper than read_web_page for pulling specific facts, and handles PDFs. Pass full_content=true for the whole page as Markdown. Costs 1 credit per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesThe http(s) pages to read.
objectiveNoWhat to pull out of the pages.
full_contentNoAlso return each whole page.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations only declare readOnlyHint=true; the description adds concrete behavioral context beyond that: cost (1 credit per call), PDF support, the batch limit (five), and that full_content switches output to Markdown. It doesn't describe pagination, rate limits, or failure modes on unreachable pages, so it's strong but not exhaustive.

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?

Front-loaded with the core purpose and scope, then alternatives, then output mode, then cost. No filler sentences, though the objective example adds mild redundancy with the schema description. Efficient overall.

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?

For a read-only extraction tool with no output schema, the description covers the key decision points: scope, alternatives, output format options, PDF support, and cost. Minor gaps remain around error handling and whether results are per-URL tagged, but the agent has enough to call it correctly.

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%, so the parameters are already documented. The description reinforces semantics for objective with a concrete example ('the price of each plan') and explains full_content's effect, but this is largely restating schema-level detail. Baseline 3 is appropriate when the schema carries the load.

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?

States a specific verb (extract) and resource (web pages), defines the scope (up to five public pages at once), and explicitly differentiates from the sibling read_web_page by calling out velocity and cost advantages. An agent can distinguish it from read_web_page without inspecting the schemas.

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

Usage Guidelines5/5

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

Explicitly names an alternative (read_web_page) and the condition that selects this tool: 'pulling specific facts'. Also explains the full_content fallback for whole-page retrieval and cites the cost (1 credit). This covers when-to-use, when-not, and the alternative.

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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