Get named evidence (bull/bear case)

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

The annotations already declare readOnlyHint=true and openWorldHint=true, and the description adds substantial context beyond that: the exact return structure (five grids with cells containing label, value, and tone) and the semantics of tone values ('bullish'|'bearish'|'neutral'|'unknown'). This helps the agent understand what to expect.

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, well-structured paragraph that front-loads the purpose, then details the output structure, and ends with usage guidance. Every sentence adds value, though it is a bit lengthy. It is organized and readable.

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?

Despite having a minimal schema and no output schema, the description thoroughly explains the return format, interpretation (how to assemble bull/bear cases), and usage context. It covers the essential aspects for correct invocation and result processing. Minor gaps exist around edge cases like tickers with no evidence.

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 has one parameter 'ticker' with 0% description coverage, so the description carries the burden. It implicitly defines 'ticker' as the subject of the Q-Score evidence, which is sufficient for an agent to infer its meaning. However, it doesn't provide format constraints or examples, preventing a perfect score.

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 names a specific resource ('named, two-sided evidence behind a ticker's Q-Score') and elaborates on its content, distinguishing it from sibling tools by explicitly tying it to stoquant_get_qscore and the 'evidence layer' versus the 'dimension breakdown'. This is clear and specific.

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 says when to use the tool: 'Use this AFTER stoquant_get_qscore when the user asks WHY the score is what it is', and contrasts it with the dimension breakdown, giving clear selection guidance and an alternative. This is exemplary.

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