cognigy-ai-mcp-management-server

get_knowledge_connector

Read-onlyIdempotent

Retrieve detailed configuration of a Cognigy.AI knowledge connector, including connector type, schedule, connection settings, and run status.

Instructions

Gets detailed configuration of a specific Cognigy.AI knowledge connector. Returns connector type, schedule, connection settings, and run status.

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": {
      "connectorId": "<connectorId>",
      "knowledgeStoreId": "<knowledgeStoreId>"
    },
    "name": "get_knowledge_connector"
  }
}

Input Schema

Name	Required	Description	Default
`connectorId`	Yes	The knowledge connector ID to retrieve
`knowledgeStoreId`	Yes	The knowledge store ID containing the connector

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.4Jul 27, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. Description adds value by specifying what data is returned (connector type, schedule, connection settings, run status), enriching transparency 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?

Two sentences, concise, front-loaded with the action and resource. Every sentence earns its place without redundancy.

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 simplicity of the tool (2 required params, no output schema), the description adequately lists the returned fields. However, without an output schema, more details on the structure of the response could improve completeness.

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 both parameters described. Description does not add further semantic meaning beyond the schema (e.g., how to obtain connectorId or knowledgeStoreId), 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?

Description uses a specific verb ('Gets') and identifies the resource ('detailed configuration of a specific Cognigy.AI knowledge connector'), clearly distinguishing it from sibling tools like 'list_knowledge_connectors' or 'create_knowledge_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?

No explicit guidance on when to use this tool versus alternatives (e.g., list_knowledge_connectors for overview). The context of a simple getter implies usage when specific details are needed, but no when-not or exclusion statements are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server