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MaryamZi

rag-retrieval-mcp

by MaryamZi

retrieve

Search a knowledge base using embeddings and vector stores to return relevant content for retrieval-augmented generation.

Instructions

Search a knowledge base and return relevant content.

Args: query: The search query to find relevant content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It only mentions 'return relevant content' without detailing side effects, authentication requirements, rate limits, or whether the tool is read-only. This is insufficient for a tool with no annotation support.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short (two sentences) and front-loaded with the main purpose. The second sentence restates the parameter name without adding value, which could be removed. It is not overly verbose but could be tighter.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple tool (one required parameter, no nested objects) and the presence of an output schema, the description provides baseline completeness. However, it lacks context about result format, pagination, or what 'relevant content' entails, leaving some ambiguity.

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?

The input schema coverage is 0%, so the description must compensate. It describes the 'query' parameter as 'The search query to find relevant content,' which adds some semantic meaning beyond the schema's type string. However, it lacks details on expected format, length, or examples, which would be more helpful.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'search' and the resource 'knowledge base', making the tool's purpose unambiguous. However, it lacks specificity about the type of knowledge base, which could be improved.

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 guidance is provided on when to use this tool versus alternatives. Without sibling tools or usage context, the agent has no basis for deciding when this search is appropriate.

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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