get_earnings_catalyst

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

Annotations indicate read-only, open-world, idempotent, and non-destructive behavior, which the description does not contradict. The description adds valuable context about the pipeline steps (fetching, computing, extracting) that go beyond annotations, ensuring the agent understands the tool's internal operations. No contradictions found.

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 lists actions, the second states returns. Every phrase adds value, with no redundancy or fluff. The structure is clear and efficient.

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 has no output schema, so the description must cover return values. It states 'Returns catalyst score and actionable signal,' which is concise but sufficient given the complexity of the NLP pipeline. Annotations already cover safety and idempotency, so the description is reasonably complete for a retrieval tool. More detail on output structure could improve it.

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?

Schema has two parameters: 'symbol' (required, no description) and 'quarter' (optional, with example). Schema description coverage is 50%. The tool description implicitly associates 'symbol' with fetching SEC filings and 'quarter' with prior quarter comparison, but does not explicitly document parameter formats or valid values. This provides some context but not thorough semantics.

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 runs an earnings NLP pipeline, listing specific steps: fetch SEC filing, compute FinBERT sentiment, extract themes (guidance, EPS beat/miss, capex), and detect tone shift. It concludes with returns of catalyst score and actionable signal, making the purpose highly specific and distinct from many other 'get_*' 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 the tool is used for earnings analysis by detailing the pipeline steps. However, it does not explicitly state when to use it versus alternatives or provide exclusions. Given the context of many sibling tools, the usage is clear but could be improved with direct guidance on when not to use it.

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