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TsvetanG2

cognigy-ai-mcp-management-server

list_nlu_connectors

Read-onlyIdempotent

Lists NLU connectors to view integrations with external NLU services for intent recognition, including Dialogflow, LUIS, and custom solutions.

Instructions

Lists Cognigy.AI NLU connectors. NLU connectors enable integration with external NLU services like Dialogflow, LUIS, Watson, or custom solutions for intent recognition.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skipNoNumber of items to skip for pagination
limitNoMaximum number of NLU connectors to return (default: 25, max: 100)
filterNoFilter NLU connectors by name
projectIdNoFilter NLU connectors by project ID
Behavior4/5

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

Annotations already indicate readOnly, idempotent, non-destructive, and open world hints. The description adds useful domain context about NLU connectors integrating with external services like Dialogflow, LUIS, Watson, which helps the agent understand the resource type beyond the schema.

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 sentences with no wasted words. The first sentence immediately states the action and resource, making it easy to parse.

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?

The description covers purpose and provides context about NLU connectors. However, it lacks mention of pagination or filtering behavior, though these are in the schema. For a simple list tool with complete annotations, this is mostly sufficient.

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%, describing all 4 parameters (skip, limit, filter, projectId). The description adds no additional meaning to these parameters, so baseline 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 tool lists NLU connectors, specifying the resource and action. It distinguishes from sibling list tools that list other resources (e.g., list_projects, list_flows) and from get_nlu_connector which retrieves a single connector.

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

Usage Guidelines2/5

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

The description does not provide any guidance on when to use this tool versus alternatives, such as get_nlu_connector for a specific connector or search_resources for broader search. No exclusions or context for when not to use it.

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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