DocImprint

Ask a Question About a URL

url.qa

Read-only

Fetch a public HTTPS URL and answer a specific question about its content. Lean mode — no bundle stored. Use when you have a precise question about a web page. For a broad summary, use url.summarize. For multi-document Q&A, use collection.ask instead. Returns: { url, answer, answer_cited: { value, confidence, citations[] }, confidence: "high"|"medium"|"low", truncated } Example prompts:

"What is the refund policy at https://docs.example.com/policy?"
"Look at [URL] and tell me what the delivery terms are."
"Answer this question based on the content of [URL]: [question]."

Input Schema

TableJSON Schema

Name	Required	Description
`url`	Yes	Public HTTPS URL to fetch and question. Example: "https://docs.example.com/policy"
`question`	Yes	Specific question to answer from the page content. Example: "What is the refund policy?"
`max_tokens`	No	Input length cap (1 token ≈ 4 chars). Truncates fetched page content, not the answer. Example: 4000

Output Schema

TableJSON Schema

Name	Required	Description	Default
`url`	Yes
`answer`	No
`truncated`	Yes
`confidence`	No
`answer_cited`	No

TDQS

A5/5.0

Behavior5/5

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

Beyond annotations (readOnlyHint, openWorldHint), the description discloses 'lean mode — no bundle stored', explaining that content is not persistently stored. It also clarifies that max_tokens truncates fetched page content, not the answer, and lists return fields including confidence and truncated flag.

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 concise with three clear sections: what it does, when to use it, and example prompts. Every sentence adds value, and the structure is easy to parse. The return format is compactly listed.

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 the tool's complexity (fetching and QA) and the presence of an output schema (implied by the return description), the description is complete. It covers purpose, usage, behavior, and parameters without gaps. The sibling context is also well addressed.

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

Parameters5/5

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

The input schema has 100% coverage with descriptions for all parameters. The description adds further value by including example prompts that demonstrate usage, and explains the effect of max_tokens (truncates input, not output), which is not apparent from the schema alone.

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 it fetches a public HTTPS URL and answers a specific question. It distinguishes itself from siblings like url.summarize (broad summary) and collection.ask (multi-document Q&A), making the purpose unambiguous.

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?

The description explicitly tells when to use this tool (precise question about a web page) and when to use alternatives (url.summarize for broad summary, collection.ask for multi-document Q&A). This provides clear decision criteria for the AI agent.

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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TDQS

A4.5/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose, grouped by domain (account, bundle, collection, document, job, receipt, url). Descriptions and naming make it easy to differentiate between similar tools like url.extract vs document.extract_text or collection.search vs collection.ask.

Naming Consistency5/5

All tools follow the same prefix.group_action pattern in snake_case (e.g., account.quota, bundle.get, collection.create). No mixing of conventions, making the API predictable and easy to navigate.

Tool Count5/5

With 22 tools, the server covers a comprehensive set of operations for document and evidence management. Each tool serves a specific purpose, and the count feels well-scoped without being bloated or sparse.

Completeness3/5

The tool surface lacks explicit create and delete operations for bundles and collections. Bundles appear to be created externally, and there is no tool to remove a bundle or collection. This is a notable gap given the server's stated purpose.

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