get_analysis_earnings

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

No annotations are provided, so the description carries the full behavioral disclosure burden. It adds a useful caveat about ETFs/mutual funds possibly returning empty results and states the tool returns records, implying read-only. However, it does not disclose pagination behavior, potential authorization requirements, or what happens for invalid symbols—details an agent might need for robust invocation.

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 sentences long and front-loaded with the core purpose. Each sentence earns its place: content scope, filtering capabilities, asset-class caveat, and intended use cases. No redundant filler or repetition of schema details.

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 no output schema, the description adequately outlines return contents by naming the metric categories included. It addresses the key edge case of non-stock assets. However, it could explicitly mention pagination (limit/offset) or default behavior for date filters, but the schema already documents these, so the description covers the most critical operational context without overburdening the text.

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 the baseline is 3. The description reinforces the meaning of 'types' by listing record types and clarifies 'methodologies' via 'GAAP/normalized', but it does not explain complex parameters like filters, sort_by, or comparison_symbols. Since the schema already handles those, the description adds only marginal semantic value beyond the structured fields.

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 returns earnings and estimate records for an asset, listing specific data categories (EPS actuals, earnings vs. revenue, EPS trends, revisions, growth). It distinguishes itself from siblings by explicitly mentioning earnings analytics and the 'Earnings & Estimates dashboards', making its purpose unmistakable.

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 use case: 'Use for populating Earnings & Estimates dashboards or feeding earnings analytics pipelines.' It also gives a when-not-to-use hint by stating ETFs and mutual funds may return empty results. However, it does not explicitly name alternative tools for non-stock instruments or compare with related get_analysis_* tools.

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