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answer_question

Grounded answer with inline [n] citations: searches the web, fetches sources, and answers strictly from them. Price: $0.05

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "answer_question"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesThe question to answer
sourcesNoHow many web sources to search, fetch, and cite

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed8 schema fields changed
    • addedInput schema / properties / query / example
      Added value: +"What is the x402 payment protocol?"
    • addedInput schema / properties / query / maxLength
      Added value: +500
    • addedInput schema / properties / query / minLength
      Added value: +1
    • addedInput schema / properties / sources / default
      Added value: +3
    • changedInput schema / properties / sources / description
      Previous value: -"Web sources to search, fetch, and cite (1-5, default: 3)"New value: +"How many web sources to search, fetch, and cite"
    • addedInput schema / properties / sources / example
      Added value: +3
    • addedInput schema / properties / sources / maximum
      Added value: +5
    • addedInput schema / properties / sources / minimum
      Added value: +1
  2. First observed

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose real behavior: it performs a multi-step search/fetch pipeline, answers strictly from retrieved sources (a grounding constraint that affects trust in the output), formats inline citations, and surfaces a $0.05 price. Missing are latency expectations, failure behavior when no sources are found, and any auth requirement.

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?

Two compact clauses with zero filler: the capability and output format lead, followed by the cost disclosure. Every element (grounding, citation format, price) earns its place and the most decision-relevant facts come first.

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 2-parameter tool with no output schema and no annotations, the description covers what it does, how the output is cited, and the cost. It stops short of positioning against deep_research or explaining what happens on low-quality/empty source results, which is the main remaining gap.

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 description coverage is 100%, so both 'query' and 'sources' are already fully documented with type, bounds, defaults, and examples. The description adds only the generic notion of fetching sources and no syntax or format detail beyond the schema, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource (answers a question) plus the mechanism (searches the web, fetches sources, answers strictly from them) and the output shape (inline [n] citations). This distinguishes it from raw retrieval siblings like search_web or fetch_webpage, but it never explicitly contrasts itself with deep_research, the closest-looking alternative.

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

Usage Guidelines3/5

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

Usage is implied — reach for this when you want a synthesized, cited answer rather than raw pages — but there is no explicit when-to-use statement, no exclusions, and no named alternative such as deep_research or search_web. The agent must infer the routing decision from the sibling list alone.

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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