cognigy-ai-mcp-management-server

get_knowledge_store

Read-onlyIdempotent

Retrieve detailed configuration of a knowledge store, including settings, language, embedding model, and source counts. Use this to understand your store's setup.

Instructions

Gets detailed configuration of a specific Cognigy.AI knowledge store. Returns store settings, language, embedding model, and source counts.

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": {
      "knowledgeStoreId": "<knowledgeStoreId>"
    },
    "name": "get_knowledge_store"
  }
}

Input Schema

Name	Required	Description	Default
`knowledgeStoreId`	Yes	The knowledge store ID to retrieve

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 already declare readOnlyHint and idempotentHint, covering safety. The description adds value by specifying the exact configuration details returned (language, embedding model, source counts), exceeding annotation information.

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?

A single sentence that is efficient, front-loaded with the action and result, and contains no redundant information.

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?

For a simple read operation with robust annotations and a single parameter, the description is sufficiently complete. It lists key return fields, though it omits error handling or prerequisites, which are less critical here.

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 a clear description for 'knowledgeStoreId'. The description adds no further parameter meaning beyond what the schema provides, 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 'gets detailed configuration' and lists returned information (store settings, language, embedding model, source counts), effectively distinguishing it from siblings like 'delete_knowledge_store' and 'list_knowledge_stores'.

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 implies usage for retrieving details of a specific knowledge store, and the sibling context provides clear alternatives. However, it lacks explicit when-to-use or when-not-to-use guidance.

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

Deploy Server