Read usage records for a date range up to one year, optionally grouping by dimension to analyze trends.
Read usage for a date range of no more than one year.
| Name | Required | Description | Default |
|---|---|---|---|
| to_date | Yes | YYYY-MM-DD | |
| group_by | No | Optional API grouping dimension. | |
| from_date | Yes | YYYY-MM-DD |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.2.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the maximum date range, which is a useful behavioral constraint, but it does not elaborate on other behaviors like error handling, rate limits, or authentication. Since annotations carry most of the burden, a 3 is appropriate – the description adds some context beyond the structured data but not rich detail.
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 zero waste. It communicates the verb, resource, and a key constraint efficiently. Every word earns its place.
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?
An output schema exists, so return values are documented separately. The description covers the purpose and the critical range constraint, which is sufficient for a simple read-only tool. It doesn't mention edge cases or errors, but given the simplicity and annotation coverage, it is complete enough for an agent to invoke 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?
The schema covers 100% of parameters with descriptions (e.g., 'YYYY-MM-DD' for dates and 'Optional API grouping dimension' for group_by). The tool description adds the crucial constraint that the date range must be ≤1 year, which is not present in the schema. This goes beyond the baseline and helps the agent validate inputs correctly.
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 a specific verb ('Read') and resource ('usage') with a clear constraint ('date range of no more than one year'). It is unambiguous and distinct from the sibling tools, which cover other operations like health or bot management. It doesn't explicitly name a sibling alternative, but the uniqueness of 'usage' makes differentiation implicit.
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 implies when to use the tool (when needing usage data) but does not explicitly state context or alternatives. The 'no more than one year' constraint is a usage guideline for parameter selection, but there is no explicit 'use this instead of X' guidance. This is adequate but leans on inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.