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python-mcp-server

combined_search

Search knowledge graph facts and document vectors together to retrieve structured facts plus supporting context, producing evidence-backed answers from hybrid search.

Instructions

Search both the knowledge graph and document vectors.

USE WHEN: You need both structured facts and supporting context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesOriginal search query
graph_resultsYesResults from knowledge graph
vector_resultsYesResults from vector search

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0-beta

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It states the tool searches two sources, implying a read-only operation, but it does not disclose any potential side effects, result structure, or limitations (e.g., how results are combined, whether there is pagination). For a search tool this is acceptable but not exhaustive; the description could mention that it returns a combined result set or any rate limits.

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 extremely concise: two sentences, with the primary purpose in the first and usage guidance in the second. No filler or redundancy. The most important information (what it does) is front-loaded, and the 'USE WHEN' clause is directly relevant.

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 the tool has an output schema (which likely describes return format) and the description covers purpose and usage, it is mostly complete. However, the lack of parameter documentation and the absence of behavioral notes (e.g., read-only status) leave minor gaps. For a search tool, the description is adequate but not exhaustive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate by explaining the parameters. It mentions 'query' implicitly but provides no detail about the 'limit' parameter, its purpose, or its effect. The description adds no meaning beyond the schema's basic type and default. Since it fails to document 'limit', the score is low.

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's function: 'Search both the knowledge graph and document vectors.' This is a specific verb (search) and resource (knowledge graph + document vectors), which distinguishes it from sibling tools like search_knowledge (likely just graph) and rag_search (likely just vectors). No ambiguity.

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 includes an explicit 'USE WHEN' clause: 'You need both structured facts and supporting context.' This provides clear context for when to use the tool. However, it does not explicitly mention when not to use it or point to alternatives, leaving some room for inference. It does not say 'if you only need facts, use search_knowledge.'

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