get_consensus

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context: it discloses that the earnings surprise table only covers operating profit and net income (since the FnGuide original table lacks sales), which is a specific limitation. This goes beyond the annotations without contradicting them, though it doesn't cover other potential behaviors like rate limits or return format (mitigated by output 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 concise, front-loaded with the core purpose, followed by example queries, a limitation note, and a pointer to an alternative. Each sentence earns its place, and there is no 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?

The tool is moderately complex, but with an output schema present, the description covers the essential usage scope (when to use, what data it covers, its limitation) and points to the correct sibling for missing data. An agent has enough context to call it correctly without additional assumptions.

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 provides only a string type for 'code' with no description (0% coverage). The description compensates fully by specifying the format ('종목코드 6자리') and giving an example ('005930'). This is clear and actionable, making the parameter semantics complete.

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 provides consensus data: analyst opinions, target prices, and earnings surprise (preliminary vs consensus). It gives specific example queries ('목표가 얼마야', '컨센서스', etc.) and explicitly distinguishes from get_financial for sales figures. This is a specific verb+resource with clear differentiation from a sibling tool.

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?

It explicitly lists when to use the tool (for target price, consensus, analyst opinions, earnings surprise) and when not to (for sales figures, pointing to get_financial). This provides clear context and an alternative, leaving nothing to inference.

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