api

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

A4.4/5.0

Behavior4/5

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

Annotations already provide readOnlyHint=true and openWorldHint=true, indicating safe read operation. The description adds value by disclosing 'Lean mode — no bundle stored', and detailing return fields including confidence and truncation. No contradictions with annotations.

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 concise, front-loaded with the main purpose, and structured with return fields and example prompts. Only minor redundancy (e.g., 'Example:' wording), but overall efficient.

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 has an output schema (implied) and 3 parameters, the description lists all key return fields (url, answer, answer_cited, confidence, truncated) and provides example prompts. It is complete for the agent to select and invoke the tool 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%, with all three parameters described in the input schema. The description does not add new parameter semantics beyond what the schema already provides, so baseline 3 is appropriate.

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 the tool fetches a public HTTPS URL and answers a specific question, using a precise verb and resource. It distinguishes itself from siblings url.summarize and collection.ask, making its 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?

Explicitly states when to use: 'when you have a precise question about a web page'. Also provides clear alternatives: 'For a broad summary, use url.summarize. For multi-document Q&A, use collection.ask instead.' This gives both inclusion and exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All api tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose, with no overlap. The category prefixes (account, bundle, collection, document, job, receipt, url) and specific action names (get, notarize, verify, create, list, etc.) ensure that an agent can unambiguously select the correct tool for any task.

Naming Consistency5/5

All tools follow a consistent category.action or category.action_noun pattern using snake_case (e.g., bundle.get, collection.add_document, url.translate). No mixed conventions or irregular names, making the pattern predictable and easy to learn.

Tool Count4/5

With 22 tools, the set is somewhat large but each tool addresses a distinct need within a broad domain (evidence management, document AI, collections, URL processing, job tracking, receipts). The count is slightly above the typical well-scoped range but still reasonable given the scope.

Completeness2/5

The tool surface has notable gaps: there is no tool to create or delete an evidence bundle, nor to update collections or bundles. The core workflow of creating a bundle from a document is missing, and the lifecycle is incomplete, which would likely cause agent failures in typical use cases.

Resources

Need Help?