query_data

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

No annotations are provided, so the description must disclose behavior. It only mentions natural language querying and topics, but does not state whether it is read-only, what it returns, or any constraints. This leaves the agent without insight into side effects, permissions, or output format.

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 a single sentence that front-loads the tool's purpose and elaborates briefly. No filler or redundancy.

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?

Given the simple single-parameter tool, the description is adequate for basic selection but lacks details on return behavior, which is critical since no output schema exists. It does not explain what the agent can expect from the query response.

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 schema coverage is 100% with a well-described 'prompt' parameter including examples. The description adds context about allowed query topics but does not add new parameter-level details beyond the schema.

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 queries the Rice Stock Data Portal using natural language, with explicit examples of query types (stocks, financial metrics, sectors). It uses a specific verb and resource, distinguishing it from generic tools despite no sibling tools.

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 implies usage for natural language queries about stock data but does not explicitly state when not to use it or mention alternatives. Since there are no sibling tools, no exclusions are necessary, but it lacks explicit 'when to use' language.

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