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by ai.rempa
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Get an answer

get_answer
Read-only

Get one AI answer in full: the prompt, the model and its version, the country, the complete answer text (markdown), the brands it names with their position, sentiment (0 to 100) and the reason for it, and its sources (URL, website, title). Use it to read what an AI assistant actually says after spotting an answer with list_answers. Read-only.

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": {
      "project": "<project>",
      "answer_id": "<answer_id>"
    },
    "name": "get_answer"
  }
}

Input Schema

NameRequiredDescriptionDefault
projectYesThe project: its id or its address (for example "acme-france"), as returned by list_projects.
answer_idYesAn answer_id (a UUID) from list_answers.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / answer_id / description
      Previous value: -"An answer_id from list_answers."New value: +"An answer_id (a UUID) from list_answers."
    • changedInput schema / properties / project / description
      Previous value: -"The project: its id, or its address (for example \"acme-france\"). See list_projects."New value: +"The project: its id or its address (for example \"acme-france\"), as returned by list_projects."
  2. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the 'Read-only' sentence adds little beyond them. The description does usefully scope the return payload, but it says nothing about error behavior for a missing/invalid answer_id, access constraints, or latency. With annotations carrying the safety profile, this is adequate but not rich.

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 definition is front-loaded with the core action and then enumerates return fields in a single dense but purposeful sentence, followed by a short usage sentence and a read-only note. Nothing is wasted, though the payload enumeration is long enough to flirt with being a list dump.

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?

There is no output schema, so the description must explain what comes back, and it does so thoroughly with every returned field named. Combined with the clear selection guidance against list_answers, an agent has everything needed to call this 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%: project is documented as an id or address from list_projects, and answer_id as a UUID from list_answers. The description only reinforces that context through the list_answers reference and adds no syntax or format detail beyond the schema. 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 gives a specific verb and resource ('Get one AI answer in full') and immediately enumerates the full payload: prompt, model version, country, answer markdown, brands with position/sentiment/reason, and sources. It also explicitly distinguishes itself from the sibling list tool by framing it as the detail read after list_answers.

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

Usage Guidelines4/5

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

It states a clear usage pattern: use this to read what an AI assistant actually says after spotting an answer with list_answers. That names the alternative and the triggering condition. It stops short of explicit exclusions against the other get_* siblings, so it is strong but not exhaustive.

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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