Mencoro

Report a wrongly analysed AI answer

report_ai_response

Flag a captured AI answer whose analysis is wrong - most often a brand or competitor mention that was missed - so Mencoro reviews it. type "missed_mention" or "other"; comment says what is wrong. Find the ids with list_ai_responses.

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": {
      "organizationId": "<organizationId>",
      "projectId": "<projectId>",
      "trackedQueryId": "<trackedQueryId>",
      "aiResponseId": "<aiResponseId>",
      "type": "<type>"
    },
    "name": "report_ai_response"
  }
}

Input Schema

Name	Required	Description	Default
`type`	Yes
`comment`	No
`projectId`	Yes
`requestId`	No
`aiResponseId`	Yes
`organizationId`	Yes
`trackedQueryId`	Yes

Output Schema

Name	Required	Description
`type`	Yes	The kind of problem reported
`comment`	No	The note as stored. An empty or whitespace-only comment is stored as null.
`accepted`	Yes	True once the report has been accepted for review. It is never false: a report that could not be delivered answers with an error instead.
`aiResponseId`	Yes	The captured AI answer the report is about
`trackedQueryId`	Yes	The tracked query that answer was captured for
`missedBrandNames`	Yes	Brand names reported as missed, de-duplicated and trimmed.

Schema Changelog

Changes observed during successful MCP inspections.

AddedSep 28, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare this is a non-read-only, non-destructive, non-idempotent mutation. The description adds genuine context beyond them: the call routes into a Mencoro human review workflow, and repeated flags are not deduplicated (consistent with idempotentHint=false). It does not state auth/permission needs or rate limits.

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?

Short and front-loaded, leading with the core action and its typical trigger before the parameter mechanics. Slightly choppy use of fragments ('comment says what is wrong') but no wasted sentences.

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?

An output schema exists, so return values need no explanation, and the description covers the action, the enum, the comment, and id discovery. It is slightly thin on the relationships among the multiple id parameters, but sufficient to 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 description coverage is 0%, so the description must carry the load. It clarifies the `type` enum values and the `comment` field's purpose, and tells the agent where ids come from, but leaves organizationId, projectId, trackedQueryId, aiResponseId and requestId entirely unexplained.

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 resource ('Flag a captured AI answer whose analysis is wrong') plus the dominant reason ('brand or competitor mention that was missed'), making it immediately distinguishable from the read-only sibling list_ai_responses, which it names.

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?

Gives clear context for use (wrong analysis, especially missed mentions) and points to list_ai_responses as the way to obtain the needed ids. It lacks an explicit when-not or exclusion (e.g., what does not qualify as reportable), so it falls short of a 5.

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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