analyze_earnings

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

The description discloses use of Groq (LLaMA 3.3-70B) with NVIDIA NIM fallback, adding behavioral context. But it omits authentication needs, rate limits, or failure behavior, which are important given no annotations.

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 three lines, efficiently covering purpose, outputs, and technical backend. No redundant information, each sentence adds value.

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 description lists return outputs (trend assessment, surprise %, quality rating, insights), compensating for missing output schema. However, it lacks prerequisites like data availability or limitations on time periods.

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 coverage is 100% and the schema already describes the 'symbol' parameter with examples. The description adds no additional parameter meaning beyond what the schema provides.

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 performs AI analysis of earnings history, listing specific outputs (trend, EPS surprise quality, forward outlook). It distinguishes from siblings like 'get_earnings' by emphasizing AI interpretation rather than raw data retrieval.

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 when AI-driven earnings analysis is needed, contrasting with 'get_earnings' for raw data. However, no explicit when-to-use, prerequisites, or exclusions are provided.

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