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RAG knowledge search

rag_search
Read-only

Semantic search across the authenticated user's vectorized documents and knowledge base. Returns the most relevant text chunks with similarity scores.

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": "rag_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum number of chunks to return (1-50).
queryYesNatural-language search query.
group_keyNoOptional document group key to scope the search to a single document/collection.
min_scoreNoMinimum similarity score (0-1) a chunk must reach to be returned.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds useful scope ('authenticated user's' documents only) and the return shape (chunks with similarity scores), but says nothing about behavior on empty results, ranking/merge behavior, or cost/latency of an embedding search.

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, zero filler, with the scope constraint front-loaded before the return description. Nothing is repeated from the schema or annotations.

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 read-only search tool with no output schema, the description does state what is returned (relevant text chunks plus similarity scores), which covers the main gap. The remaining omission is how this relates to the near-identical sibling tools, not anything an agent needs to invoke it correctly.

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 description coverage is 100%, with limit, min_score, group_key, and query each documented in the schema. The description adds no syntax or tuning guidance beyond that, so the baseline of 3 applies.

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 gives a specific verb and resource ('Semantic search across the authenticated user's vectorized documents and knowledge base') and states what comes back. It is clear on its own, but it never distinguishes itself from the close siblings rag_similar and memory_search, which an agent must choose between.

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?

There is no statement of when to use this tool versus rag_similar (near-duplicate lookup) or memory_search (stored memories). The only implicit cue is the word 'semantic,' leaving the agent to guess at routing.

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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