get_statistics_day

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

No annotations are provided, so the description carries full responsibility for behavioral transparency. It covers parameter formats but omits important details such as whether the tool is read-only, rate limits, data freshness, or the nature of the statistics (e.g., aggregated counts). The description does not disclose behavioral traits beyond basic parameter usage.

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 very concise, using a clear docstring format with one line per parameter. Every sentence adds value, and there is no redundant information. It is well-structured for quick parsing by an AI agent.

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?

Given that there is an output schema, the description does not need to detail return values. However, it lacks explanation of what metrics are available in each group and does not describe the output structure or potential errors. For a tool with a sibling set including other statistics tools, the description could better contextualize its role.

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%, so the description compensates by explaining each parameter: entity options, format for IDs and dates, and an optional metrics group. This adds significant value beyond the raw schema. However, it could be improved by listing the exact allowed values for entity and metrics groups, and providing examples for clarity.

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 states 'Daily statistics' which clearly indicates the tool retrieves daily statistics for various entities. The name and parameter list further clarify the purpose. However, it does not explicitly differentiate from siblings like get_statistics_breakdown or get_statistics_summary, which could confuse an agent about which tool to use for different types of statistics queries.

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 provides parameter details but offers no guidance on when to use this tool versus alternatives such as get_statistics_breakdown or get_statistics_summary. There is no mention of typical use cases, prerequisites, or contexts where this tool is preferred, leaving the agent to infer usage from the tool's name alone.

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