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

Even with readOnlyHint=true, the description adds significant non-obvious behavior: model refusals are valid results, not gaps, and must not be substituted or presented as missing data. This is critical operational guidance beyond the annotations and directly shapes how an agent should interpret and present output.

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 front-loads what the tool returns, and the second delivers a critical behavioral caveat. No wasted words, and every clause contributes to safe and correct usage.

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?

For a read-only tool with one fully documented parameter and an existing output schema, the description covers the core return content, the range of models, and an essential interpretation rule. No significant information gaps remain for an agent to invoke and use this tool correctly.

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% for the single ticker parameter, which already defines it with an example. The description adds no additional parameter-level meaning beyond what the schema provides, so the baseline score of 3 is appropriate.

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 'every valuation model's verdict plus the fair-value range' and enumerates the models (DCF, earnings-power value, Graham number, P/E reversion). This specific verb+resource phrasing distinguishes it from sibling tools like get_fundamentals or get_expected_return.

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 multi-model intrinsic value is needed, and the interpretation guidance (refusals are meaningful) helps the agent handle results. However, it does not explicitly state when to prefer this tool over siblings or provide exclusion criteria, leaving the when-to-use context merely implied.

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