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Iteksmart

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by Iteksmart

ragflow_query

Query a RAGflow knowledge base using natural language. Filter results by dataset IDs for targeted information retrieval.

Instructions

Query the RAGflow knowledge base with a natural-language question. Optionally filter to specific dataset IDs.

Requires scope: integrations:rag:read. Every call governed by Arbiter constitutional policy and sealed with a ProofLink cryptographic receipt.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYesNatural-language question (max 2000 chars)
dataset_idsNoOptional dataset filter
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses required scope (integrations:rag:read), governance by Arbiter policy, and ProofLink receipt sealing, adding valuable behavioral context beyond the bare action.

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 concise sentences conveying purpose and optional filter, plus a third line for behavioral context. Front-loaded and no unnecessary words.

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?

No output schema and description doesn't specify return format or example, which is significant for a query tool. Lacks information on expected output or limitations.

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 coverage is 100% with descriptions for both parameters. The description only repeats the optional filter, adding no new semantic meaning beyond the schema.

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 verb 'Query' and the resource 'RAGflow knowledge base', distinguishing it from sibling tools like 'ragflow_health'.

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?

Implied usage by describing natural-language querying and optional filtering, but lacks explicit when-to-use or when-not-to-use guidance compared to alternatives.

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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