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rag-starter

rag-starter

by cstamigo-droid
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

rag_search

Read-onlyIdempotent

Retrieve relevant passages from local documents for a question, each with a citation tag, to ground answers in source evidence.

Instructions

Return the most relevant passages for a question, each with a citation.

Use this to ground your own answer: read the passages and cite their [tags].

Examples: - "What is the refund policy?" -> query='refund policy' - "How do I reset my password?" -> query='reset password'

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": {
      "params": "<params>"
    },
    "name": "rag_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
paramsYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnly/ idempotent/ non-destructive/ closed-world, so the safety profile is covered. The description usefully adds that results carry citations and should be cited by [tags], but it discloses nothing about ranking quality, empty-result behavior, or latency, so it only modestly exceeds the structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the purpose in sentence one, followed by usage guidance and two compact examples; every element is short and earns its place. Sentence two slightly overlaps the citation point already made in sentence one, a minor redundancy.

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?

An output schema exists, so return-shape detail is not required, and the single nested parameter is well covered by the schema. The notable gap is sibling routing: with rag_answer and rag_ingest present, the description should state plainly when to prefer this tool, and it only implies it.

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 description contributes query-narrowing examples ('refund policy', 'reset password') that clarify how to phrase the query parameter. It says nothing about k (default/max) or response_format, so coverage of the remaining parameters rests entirely on the schema.

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 first sentence gives a specific verb and resource — returns relevant passages with citations — so an agent knows exactly what comes back. It does not explicitly name or distinguish itself from the sibling rag_answer, so the reader must infer the split from 'ground your own answer.'

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?

'Use this to ground your own answer: read the passages and cite their [tags]' implies the when-to-use case (agent composes the answer itself rather than calling rag_answer), and the query examples show input formulation. However, no alternative is named and there is no explicit 'use rag_answer instead when you want a synthesized answer' 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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