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DryadAI

dryad-rag-mcp

Official
by DryadAI

Query the RAG index

rag_query

Ask a question to retrieve the most relevant document chunks with source citations and similarity scores.

Instructions

Ask a question against the indexed documents and get back the most relevant chunks with their source file and a similarity score, for citation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_kNoNumber of chunks to return
questionYesThe question to search for
Behavior3/5

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

Given no annotations, the description carries the full burden. It discloses what the tool returns (chunks, source file, similarity score) and its purpose (citation). However, it doesn't explicitly state read-only behavior, potential side effects, or limitations (e.g., no matching results, index readiness). It's adequate but leaves some behavioral traits undisclosed.

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 single sentence, concise and front-loaded with the primary action. It contains no filler or redundant information. Every clause adds value: action, scope, output, and purpose.

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 simple query tool with two parameters and no output schema, the description is complete enough. It explains the return payload (chunks with source and score) and the intended use (citation). It doesn't address edge cases like empty results, but that's not critical given the tool's simplicity and the high schema coverage.

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 input schema has 100% description coverage for both parameters (question and top_k), so the description doesn't need to repeat them. The description adds no parameter-specific meaning beyond the schema, but the baseline of 3 applies because the schema already documents the parameters thoroughly.

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: 'Ask a question against the indexed documents and get back the most relevant chunks with their source file and a similarity score, for citation.' It uses a specific verb ('ask') and describes the resource (indexed documents) and output (chunks, source file, similarity score). This distinguishes it from sibling tools rag_health and rag_ingest, which clearly serve different purposes.

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 implies usage: it is the tool for querying the RAG index with a natural language question. While it doesn't explicitly exclude alternatives or state when-not-to-use, the clear action ('Ask a question') and context ('against the indexed documents') provide sufficient guidance. It doesn't mention rag_ingest or rag_health, but the different verbs in the sibling names make the distinction obvious.

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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