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

Annotations only declare readOnlyHint=true, but the description adds substantial behavioral context: no live quotes, per-section as_of timestamps, free-tier limits and overage pricing, and the non-recommendation stance. This goes well beyond what the 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 long but every sentence carries decision-relevant information: what the tool returns, what it excludes, how to trim output, and cost/access details. It is front-loaded with the core purpose, though the pricing details could arguably be moved to a separate note.

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?

Combined with the output schema and annotations, the description fully covers what the tool does, what it does not do, how to select sections, behavioral caveats, and usage limits. An agent has everything needed to decide when to call it and how to invoke it 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% and the sections parameter already documents the default set and explicit opt-in for inside_sentiment. The description reinforces this and adds the useful gloss that inside_sentiment means 'how the business reads from the inside,' providing slight extra meaning beyond the schema.

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 opens with a specific, memorable framing: everything QuantApe derives about ONE stock that a quote API can't tell you. It enumerates concrete sections and explicitly contrasts itself with quote APIs, making its scope and differentiation clear.

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?

It explicitly says to use a quote API for live quotes, market cap, and valuation ratios, and instructs the agent to pick sections to keep the answer small. It also flags inside_sentiment as opt-in and describes the tool as descriptive rather than a recommendation, giving clear decision rules.

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