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rag_search

Retrieve relevant notes from a local Markdown knowledge base with per-note scores and traceability. Use excerpts as candidates; call read_note before citing.

Instructions

Retrieval with per-note scores and traceability fields. Excerpts are candidates: call read_note before citing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
pathsNo
queryYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
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 discloses that excerpts are non-authoritative candidates and warns to verify with read_note, which is valuable. But it does not explicitly state whether the operation is read-only, how scores or traceability fields are structured, or any limitations such as pagination or indexing requirements.

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 two tight sentences with no filler. The first states the core purpose, and the second provides a critical usage warning, so every word earns its place.

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

Completeness2/5

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

With no annotations, no output schema, and zero schema description coverage, the description leaves too many gaps. An agent still lacks clarity on parameter meaning, return structure, and how this tool relates to the sibling search_notes, despite the useful excerpt warning.

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

Parameters1/5

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

Schema description coverage is 0%, and the description provides no explanation of query, limit, or paths semantics. An agent sees only parameter names and defaults, with no guidance on expected values or behavior.

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 states the tool performs retrieval with per-note scores and mentions note-related output, making the core purpose reasonably clear. However, it does not distinguish rag_search from the sibling search_notes tool, so an agent may not know when to choose one over the other.

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?

It gives a clear post-retrieval instruction: excerpts are candidates, so call read_note before citing. This is useful, but there is no guidance on when to use rag_search versus search_notes or when not to use this tool.

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