mcp-knowledge-server

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search_documents

Find documents by semantic similarity to your query. Search a centralized knowledge base, optionally filter by collection, and specify the number of results.

Instructions

Search documents by semantic similarity.

    Args:
        query: The search query text.
        collection: Optional collection name to search in.
        top_k: Number of results to return (default 10).

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": {
      "query": "<query>"
    },
    "name": "search_documents"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes
`top_k`	No
`collection`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 9, 2026

TDQS

A3.7/5.0

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 that results are based on semantic similarity, but does not state whether the operation is read-only, any side effects, or error conditions. The search verb implies a non-mutating action, but this is not explicit.

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 concise docstring with a clear opening sentence and a compact arg list. It contains no unnecessary words and is well-structured for quick parsing.

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?

The tool is simple, but its relationship to similar_documents is unaddressed. The description also does not mention whether a collection must exist or how results are ordered. The output schema exists, so return details are covered elsewhere, but behavioral prerequisites are missing.

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

Parameters4/5

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

The description provides brief semantics for all three parameters: query text, optional collection name, and number of results, including the default for top_k. Since the schema has no descriptions (0% coverage), this adds meaningful value beyond the property names.

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 'Search documents by semantic similarity', which identifies the verb (search), resource (documents), and method (semantic similarity). This is specific enough to distinguish from listing or retrieving documents, though similar_documents may overlap.

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 explicit guidance is provided on when to use this tool versus similar_documents or list_documents. While semantic similarity implies query-based search, there is no mention of alternatives, exclusions, or specific use cases.

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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