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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds the breadth of returned data but does not disclose data vintage, update frequency, potential nulls, or other operational behavior. It is consistent with annotations and adds moderate context beyond them.

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 definition is well-structured: a one-sentence purpose, a compact enumeration of return fields, and concrete example queries. It is longer than the get_calls example, but the field list is substantive and earns its place; there is no filler or repetition.

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?

With no output schema, the description compensates by listing all major return categories, giving the agent expectations for what the result will contain. For a simple read-only profile tool, this is largely sufficient. It does not explain how company names map to ticker symbols or caveat historical data freshness, but those are minor relative to the provided context.

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 documents symbol as 'Stock ticker symbol (e.g., "AAPL")'. The description's examples like 'tell me about Apple' imply user-facing natural language but do not clarify that the tool itself likely expects the ticker symbol, so it adds minimal semantics over the schema without contradicting 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?

Description opens with a specific verb and resource: 'Get company profile and financial fundamentals' and then enumerates a precise list of returned data points. This content-rich field list distinguishes it from nearby siblings like stock_quote or stock_history, which focus on price/trading data rather than fundamentals like EBITDA, profit margins, and shares outstanding.

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?

Gives clear usage guidance by listing concrete natural-language prompts and concluding with 'or any company research question.' This tells the agent when to use the tool, but it does not explicitly state when not to use it or name alternatives like stock_quote for price-only queries.

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