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Answer a Question

answer
Read-onlyIdempotent

Generates a cited, prose answer to a natural-language question from fresh web results. Use it for synthesized answers; choose brief snippet-based or report depth with page reads and multiple searches.

Instructions

Answer a question in prose, grounded in fresh web results, with citations.

This is the most expensive operation this server offers. One call runs a fixed set of searches, may read several pages, and then makes one model call over what it retrieved -- so use it when you want a synthesised answer, and use search when you would rather read the sources yourself.

The answer is written by a model, from the retrieved material only. sources is not the model's list: it is built by the server from the results the prose actually cited, so a URL the model invented is not in it. Read the prose against sources -- every claim should be traceable to one of them -- and read notice, which says when it is not: an answer with no citations, or citations the server dropped because they named nothing it retrieved.

depth is the one cost knob you control. brief (the default) runs a single search and answers from snippets. report runs up to three searches and reads up to three pages first, which is slower and costs the operator several times as much. Retrieval is never driven by the model: the same queries always run, so an answer costs a bounded amount rather than whatever the model decides.

Input Schema

NameRequiredDescriptionDefault
depthNo`brief` (the default) runs one search and answers from snippets. `report` runs up to three searches and reads up to three pages before answering, which costs the operator several times as much. This is the only cost knob you control.
queryYesThe question. Natural-language questions work well.
recencyNoRestrict retrieval to pages published in the last day, week, month or year. A provider without a date filter says so in `notice` rather than pretending it filtered.
languageNoThe language retrieval should be in, as an ISO 639-1 code (`en`, `de`); a region is not this field's job and it does not take one. A provider with no language parameter says so in `notice` rather than pretending it filtered.
providerNoPin retrieval to one backend, exactly as `search` does: that provider or an error. Omit to use the server's default, with its configured fallbacks. Not every provider is enabled on every deployment.
safe_searchNo`true` asks each backend to filter adult content, `false` asks it not to; omitting it means the server does not override the provider's own default, which is not the same as unfiltered. A provider without the parameter says so in `notice`.
extra_queriesNoUp to two more retrieval queries, run after `query`, for a question one phrasing would only partly cover. Used by `depth="report"`: a `brief` answer runs `query` alone.
exclude_domainsNoOmit results from these domains.
include_domainsNoRestrict retrieval to these domains, e.g. ["arxiv.org"].

Output Schema

NameRequiredDescriptionDefault
depthYes
modelYes
queryYes
answerYes
noticeNo
fetchedYes
sourcesYes
searchesYes
truncatedNo
retrieved_atNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior5/5

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

Adds substantial context beyond annotations: fixed retrieval cost, possible page reads, one model call, model-written prose restricted to retrieved material, and `sources` being server-built rather than model-generated. It explains `notice` as a signal of citation failure or dropped citations, which is important behavioral detail.

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 sibling alternative, and the later paragraphs carry useful cost and trust context. It is somewhat long and repeats some `depth` details already in the schema, but most sentences earn their place.

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?

For a complex, expensive, multi-provider answer tool with an output schema and safety annotations, the description covers the important gaps: cost, retrieval behavior, citation trust, and `notice` interpretation.

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 schema already documents all nine parameters thoroughly. The description reinforces the cost meaning of `depth` but adds little parameter semantics beyond what the schema provides, so baseline 3 applies.

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 and output form: answer a question in prose, grounded in fresh web results and citations. It also names the sibling `search` and explains the distinction: read sources yourself versus get a synthesised answer.

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 says when to use this tool and when to use `search` instead, and ties the choice to whether the caller wants a synthesised answer or direct source reading. It also explains the cost profile that drives the decision.

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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