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cognigy-ai-mcp-management-server

cognigy-ai-mcp-management-server

by TsvetanG2
OverviewSchemaSourceRelated ServersDiscussions(1)Admin
TypeScript

get_knowledge_source

Read-onlyIdempotent

Retrieve detailed information about a specific knowledge source, including metadata, processing status, chunk count, and ingestion details.

Instructions

Gets detailed information about a specific Cognigy.AI knowledge source. Returns source metadata, processing status, chunk count, and ingestion details.

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": {
      "sourceId": "<sourceId>",
      "knowledgeStoreId": "<knowledgeStoreId>"
    },
    "name": "get_knowledge_source"
  }
}

Input Schema

NameRequiredDescriptionDefault
sourceIdYesThe knowledge source ID to retrieve
knowledgeStoreIdYesThe knowledge store ID containing the source

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.4

TDQS

A3.8/5.0
Behavior4/5

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

The annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, indicating a safe, read-only operation. The description adds value by specifying the return content (metadata, status, chunk count, ingestion details), which goes beyond the annotations. No contradictions.

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 is a single, concise sentence that includes the purpose and the key return items. No unnecessary words or redundant information. Perfectly front-loaded and efficient.

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 no output schema, the description adequately explains what the tool returns (metadata, processing status, chunk count, ingestion details). For a read-only, idempotent tool with two required parameters, this is sufficient. It could optionally mention that the response is a JSON object, but that is implied.

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% - both parameters (sourceId and knowledgeStoreId) have clear descriptions in the schema. The tool description does not add any additional semantic information about the parameters beyond what the schema provides. 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 'gets detailed information about a specific Cognigy.AI knowledge source' and lists the types of information returned (metadata, processing status, chunk count, ingestion details). This distinguishes it from sibling tools like list_knowledge_sources, which lists all sources, and get_knowledge_chunk, which retrieves a chunk.

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. For example, it could mention that this tool is for retrieving full details of a single source, while list_knowledge_sources is for listing all sources. No explicit usage context is given.

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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