cognigy-ai-mcp-management-server

generate_nlu_scores

Read-onlyIdempotent

Score a test utterance against trained NLU intents to evaluate recognition accuracy before deployment. Returns ranked matches with confidence scores.

Instructions

Scores a test utterance against a Cognigy.AI flow's trained NLU intents. Returns ranked intent matches with confidence scores. Use this to test NLU recognition before deployment.

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": {
      "projectId": "<projectId>",
      "flowReferenceId": "<flowReferenceId>",
      "localeReferenceId": "<localeReferenceId>",
      "sentence": "<sentence>"
    },
    "name": "generate_nlu_scores"
  }
}

Input Schema

Name	Required	Description
`sentence`	Yes	The test utterance to score against trained intents
`projectId`	Yes	The project ID containing the flow
`flowReferenceId`	Yes	The reference ID (UUID) of the flow to score against
`localeReferenceId`	Yes	The reference ID (UUID) of the locale

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.4Jul 27, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations indicate readOnlyHint, idempotentHint, and non-destructive. The description adds that it returns ranked intent matches with confidence scores, which is consistent and provides useful behavioral detail beyond annotations.

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 concise sentences with no fluff. The description is front-loaded with the action and outcome, and each sentence serves a clear purpose.

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?

Given the tool has 4 required parameters, no output schema, but strong annotations, the description covers the essential behavioral and usage context. It could be more detailed about the return format but is adequate for an agent to select and invoke the tool.

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 parameters described. The description mentions 'test utterance' corresponding to the 'sentence' parameter but does not add significant new meaning beyond the schema descriptions.

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 it scores a test utterance against NLU intents and returns ranked matches with confidence scores. It includes a use case ('test NLU recognition before deployment') and distinguishes from siblings like train_intents or list_intents.

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?

The description provides a clear when-to-use recommendation ('Use this to test NLU recognition before deployment'). It does not explicitly mention alternatives, but the context is sufficient and differentiates from similar tools like score_utterance.

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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