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TsvetanG2

cognigy-ai-mcp-management-server

get_knowledge_chunk

Read-onlyIdempotent

Retrieve the full content, metadata, and source of a specific knowledge chunk to inspect what content is used in RAG searches.

Instructions

Gets the full content of a specific Cognigy.AI knowledge chunk. Returns the complete text, metadata, and source information. Use this to inspect what content is being used in RAG searches.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chunkIdYesThe knowledge chunk ID to retrieve
sourceIdYesThe knowledge source ID containing the chunk
knowledgeStoreIdYesThe knowledge store ID
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, etc. The description adds the RAG inspection context but does not contradict annotations. It is consistent, but the annotations carry the behavioral disclosure burden.

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, each adding value: first defines what the tool does, second gives a use case. No wasted words, front-loaded with key action.

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 the tool's purpose, output, and use case. With no output schema, it mentions return content type. Parameters are all described via schema. Slight lack of detail on output structure or error handling, but acceptable for a simple retrieval tool with strong annotations.

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 all three parameters described clearly in the schema. The description adds no additional parameter-level semantics beyond what the schema provides, so baseline score of 3 applies.

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 retrieves full content of a knowledge chunk, specifying the output includes text, metadata, and source information. It distinguishes itself from list_knowledge_chunks (which lists without full content) and other tools.

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 explicitly says 'Use this to inspect what content is being used in RAG searches,' providing a clear use case. It does not explicitly state when not to use it or list alternatives, but the context is clear enough given sibling tool names.

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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