Earnings Call Summary

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds valuable behavioral context: it states the output is AI-generated and grounded in the company's own filings, implying potential AI limitations and source reliability. This goes beyond what annotations provide.

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 sentence that front-loads the core purpose and then details what the output includes. It is concise without being terse, and every clause adds value. It could be slightly shorter but is appropriately sized.

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 tool has a simple purpose, 100% schema coverage, and no output schema, the description is sufficiently complete. It explains what the tool returns and its intended use case. A minor gap is that the symbol parameter is marked 'REQUIRED' in its schema description but the schema does not enforce it, which could cause ambiguity, but the description clearly flags 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 description coverage is 100%, so all parameters (symbol, limit, ticker, identifier) are documented in the schema. The description does not add any extra meaning about parameter usage, formats, or relationships beyond the schema. It simply mentions 'recent earnings' without elaborating on how limit affects results. This meets the baseline of 3 but does not exceed it.

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 retrieves AI-generated investor digests of earnings call transcripts and presentations, specifying the exact resource (earnings call summaries) and verb (get). It distinguishes itself from sibling tools like get_document_summary by focusing specifically on earnings-related content, and mentions the output structure (structured summary, highlights, risks, guidance, metrics).

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 provides a clear usage context: 'Use for qualitative fundamental research beyond the numbers.' This implies it complements quantitative tools like get_financials or get_screener_ratios, but it does not explicitly name alternatives or state when not to use it. The guidance is helpful but lacks explicit exclusions.

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