ragflow_query

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 a required scope ('integrations:rag:read') and the behavioral guarantee of a ProofLink cryptographic receipt on every call. It does not describe return format or rate limits, but for a query tool this is reasonable additional context beyond the schema.

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

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

The description is two sentences: the first states the action and optionality, the second covers authentication and governance. Every sentence earns its place with no redundancy or filler.

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

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

With only 2 parameters, no nested objects, and no output schema, the tool is low-complexity. The description covers the essential functional and security context, and the schema documents parameters fully. The lack of return-value detail is acceptable given the straightforward query action, though not ideal.

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

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

The input schema covers both parameters with clear descriptions (100% coverage). The description's mention of 'optionally filter to specific dataset IDs' merely reiterates the schema's dataset_ids parameter, adding no new granularity or syntax details. The baseline of 3 applies since the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action ('Query'), the resource ('RAGflow knowledge base'), and the input ('natural-language question'), distinguishing it from sibling tools like ragflow_health. It also mentions an optional filter, which adds specificity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly implies when to use this tool—when querying the RAGflow knowledge base—and notes an optional dataset filter. However, it does not explicitly name alternatives or exclusion scenarios, though the broad sibling set makes the intended use clear enough.

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