Compute SEC CAGR

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

Annotations declare readOnlyHint=true, and the description does not contradict this. The description adds behavioral information: it refuses non-positive or missing endpoints, and it mentions the internal method ('log-walk + Go parity') and HTTP endpoint, offering valuable detail beyond the 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 short (two sentences) and front-loads the computation verb, but the first sentence is dense with technical jargon that may obscure meaning. The second sentence is clear. It could be more accessible without losing substance.

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 tool has an output schema, so return value explanation is unnecessary. However, the description lacks prerequisites (e.g., need for pre-loaded data) and does not relate to sibling tools like lattice_arith_evaluate or query_sec_*. It is adequate for a simple computation but misses contextual cues for an agent navigating multiple tools.

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?

Input schema coverage is 100% with descriptions for all parameters. The description does not add any additional meaning or context to the parameters (e.g., format, constraints, relationships) beyond what the schema already provides. Baseline score of 3 is appropriate given the schema's completeness.

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 explicitly states it computes 'CAGR' using 'EDGAR facts', which clearly identifies the output and domain. However, jargon like 'Domain 31 lattice' and 'log-walk + Go parity' may reduce immediate clarity for an AI agent. It distinguishes itself from sibling query tools by mentioning a derived computation over raw data.

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?

No guidance is provided on when to use this tool versus alternatives such as query_financial_sec or query_sec_metric_exact. The only constraint mentioned is refusal of non-positive or missing endpoints, which is insufficient for an agent to understand prerequisites or preferred use cases.

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