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TsvetanG2

cognigy-ai-mcp-management-server

get_nlu_connector

Read-onlyIdempotent

Retrieve detailed configuration of an NLU connector, including type, settings, and connection details for integrating external NLU services.

Instructions

Gets detailed configuration of a specific Cognigy.AI NLU connector. Returns type, settings, and connection details for external NLU service integration.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nluConnectorIdYesThe NLU connector ID to retrieve
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, so safety is covered. Description adds value by specifying return content (type, settings, connection details), but misses potential error conditions or auth requirements. Acceptable given annotation richness.

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?

Single sentence, front-loaded with purpose, no unnecessary words. Every part earns its place.

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?

For a simple read tool with one required parameter and no output schema, the description is complete. It tells what the tool returns and implies it is a read operation. Annotations cover the rest.

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 covers 100% of the single parameter with a description. The tool description does not add additional meaning or usage hints beyond the schema, so 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 clearly states the tool gets detailed configuration of a specific NLU connector, including type, settings, and connection details. It distinguishes from sibling like list_nlu_connectors by specifying 'specific' and 'detailed configuration', making the purpose unambiguous.

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?

Implies usage when a specific connector ID is known, but does not explicitly state when to use this tool vs alternatives like list_nlu_connectors for listing or create/update/delete. No negative guidance or alternative naming.

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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