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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The only behavioral hint is 'pre-computed,' indicating the data is static rather than dynamically calculated. It does not explicitly state read-only behavior, data freshness, error conditions, or any side effects, leaving significant behavioral traits undisclosed.

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 a single, front-loaded sentence with no filler or redundant details. It conveys the core idea efficiently and is appropriately sized for the tool's simple interface.

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 and no annotations, the description must explain what the tool returns. It only says 'grid,' which is ambiguous about the actual data structure, value types, and how the 18 x 5 dimensions map to oversub and alpha. It also lacks any context on when this tool is preferable to sibling prediction tools, leaving the agent to guess about result interpretation and selection.

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 0%, and the description partially compensates by referencing 'stock and agent,' mapping the two parameters to their conceptual roles. However, it does not define acceptable values for 'agent,' explain the meaning of 'oversub' or 'alpha,' or describe how the parameters affect the result. This is a minimal but not complete semantic contribution.

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 identifies a specific resource: a pre-computed 18 oversub x 5 alpha prediction grid for a stock and agent. The tool name includes 'get' which provides the retrieval verb. It distinguishes itself from siblings like get_oversub and get_prediction through the specific grid structure and agent context. A fully explicit verb in the description would make it a 5.

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 given about when to use this tool versus alternatives such as get_oversub, get_prediction, or get_scatter. The description only states what the tool returns; it does not mention conditions, prerequisites, or when another sibling would be more appropriate.

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