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NumericalPie

knowledge-mcp-server

by NumericalPie

query_knowledge

Search your knowledge base using semantic search to retrieve relevant document chunks with similarity scores.

Instructions

Search the knowledge base using semantic search. Returns relevant document chunks with similarity scores.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe search query
top_kNoNumber of results to return
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the return format (document chunks with similarity scores) but does not mention any behavioral aspects such as authentication requirements, rate limits, or side effects. The description is adequate but not rich in behavioral context.

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, well-structured sentence that covers both purpose and output with no superfluous words. It is highly efficient and front-loaded with key 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 search tool with two parameters and clear sibling tools, the description is mostly complete. It includes what the tool does and what it returns (chunks with scores). However, it lacks explicit mention of scope (e.g., whether it searches all documents) and is slightly sparse given no output schema, but still sufficient.

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 covers 100% of parameters, with clear descriptions for 'query' and 'top_k'. The description does not add any additional meaning beyond the schema, so it meets the baseline but does not exceed it.

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 verb 'search', the resource 'knowledge base', the method 'semantic search', and the output 'relevant document chunks with similarity scores'. It effectively distinguishes the tool from siblings (add_document, index_url, list_documents) which perform different operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for searching the knowledge base but does not explicitly state when this tool should be used versus alternatives, nor does it provide any when-not-to-use guidance. Given the sibling tools, the usage context is clear but not elaborated.

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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