cognigy-ai-mcp-management-server

score_utterance

Read-onlyIdempotent

Score a test utterance against a Cognigy.AI flow's trained NLU intents to identify the best matching intent and its confidence score. Quickly validate utterance recognition accuracy.

Instructions

Scores a test utterance against a Cognigy.AI flow's trained NLU intents. Returns the best matching intent with confidence score. Use this to quickly test if an utterance would be recognized correctly.

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>",
      "flowId": "<flowId>",
      "utterance": "<utterance>"
    },
    "name": "score_utterance"
  }
}

Input Schema

Name	Required	Description
`flowId`	Yes	The flow ID to score against (will be converted to reference ID)
`localeId`	No	Optional locale ID (will use flow's default if not provided)
`projectId`	Yes	The project ID containing the flow
`threshold`	No	Minimum score threshold to include in results (0-1, default 0.4)
`utterance`	Yes	The test utterance to score

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.4Jul 27, 2026

TDQS

A4.3/5.0

Behavior4/5

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

The annotations already declare readOnlyHint=true, openWorldHint=true, and idempotentHint=true, indicating safe non-destructive behavior. The description adds that it returns the best matching intent with confidence, which is useful behavioral context beyond the 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?

The description consists of two concise sentences with no superfluous information. It front-loads the core functionality and then provides the intended usage.

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?

Given the tool's simplicity, no output schema, and fully documented parameters, the description is complete. It explains the purpose, input, and output without missing critical details.

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 all five parameters are already documented in the schema. The description does not add additional semantic meaning beyond the schema, so a baseline score of 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 clearly states the verb 'scores', the resource 'a test utterance against a Cognigy.AI flow's trained NLU intents', and the result 'best matching intent with confidence score'. It distinguishes itself from sibling tools like 'train_intents' and 'generate_nlu_scores' by focusing on single utterance testing.

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 says 'Use this to quickly test if an utterance would be recognized correctly', which provides clear usage context. However, it does not explicitly mention when not to use it or alternative tools for batch scoring or training, though the context is relatively clear.

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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