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fetch

Retrieve full clean page content from a recent search result when the snippet isn't enough, as markdown with title, description, and final URL. Optionally, pass a query for relevant passages.

Instructions

Fetch the full, clean content of ONE web page as markdown. Use this to READ a result you got from web_search when the snippet isn't enough and you need the page's actual text - exact pricing, full docs, the complete article. Check the search snippet first: for many factual questions it already contains the answer, and a fetch costs a page. The url MUST be one a recent search in this session returned: fetch reads your grounded sources, it is not a general-purpose scraper for arbitrary URLs. Returns clean markdown in content (set clean=false for raw HTML), plus the page's title, its description, and the FINAL url after redirects - cite that one. Pass query (what you want from the page) to also get passages: the few most-relevant verbatim slices, so a tight context window can read those instead of the whole page. This call is synchronous - it returns the content or an honest failure, and you never poll. A page we could not really read is reported as a failure, NEVER as content, so you will not be handed a bot-wall or a consent screen to summarise. Costs 1 page per successful call; a failed fetch is free.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
cleanNo
queryNo
max_tokensNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description fully discloses behavior: synchronous call, no polling, failure reported honestly rather than fabricating content, bot-walls and consent screens reported as failures, and the cost model (1 page per success, free on failure). This is unusually transparent.

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 front-loaded with the core action, then flows into usage guidance, parameter clarification, and failure semantics. Every sentence adds operational value and there is no filler or repetition.

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?

Despite having an output schema, the description adds essential context: return fields (content, title, description, final url), passage retrieval via query, grounding constraints, failure behavior, and cost. This is complete for a tool with this complexity and no annotations.

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

Parameters4/5

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. It adds meaningful semantics for url (grounded sources), clean (false returns raw HTML), and query (returns relevant passages). However, max_tokens is never mentioned, leaving one parameter unexplained.

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 a specific verb and resource: 'Fetch the full, clean content of ONE web page as markdown.' It clearly frames this as reading a search result when the snippet is insufficient, which distinguishes it from siblings like web_search and deep_search.

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?

Provides explicit when-to-use guidance: check the search snippet first, use fetch when the snippet isn't enough, and the URL must come from a recent search in the session. It also explicitly states what the tool is not ('not a general-purpose scraper'), giving a clear boundary.

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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