Requests over time for the authenticated account: zero-filled time buckets for a window (1h, 6h, 12h, 24h, 3d, 7d, 30d). Same data as the dashboard chart and CSV export; a bucket is marked partial when the window starts inside it or it is still filling.
| Name | Required | Description | Default |
|---|---|---|---|
| chain | No | Filter by chain, e.g. 'eth_mainnet' | |
| window | No | Window key: 1h, 6h, 12h, 24h (default), 3d, 7d or 30d |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral disclosure burden. It reveals key implementation details: buckets are zero-filled, a bucket is marked partial when the window starts inside it or it is still filling, and the data matches the dashboard/CSV export. These are non-obvious behaviors an agent needs to interpret the response correctly.
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 consists of two very specific sentences, the first stating the purpose and the window options, the second adding behavioral details and data source consistency. All details are actionable and not redundant — every sentence delivers a relevant fact.
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?
For a two-parameter tool with no output schema and no annotations, the description is quite complete: it explains the kind of data returned, the bucket semantics, the supported windows, and the data source. It could explicitly list the response fields, but the description gives an agent enough to call the tool and interpret the result.
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 100%: the schema documents both 'chain' and 'window' with examples and valid values. The description adds little beyond restating the window list and implying how the parameters shape the time buckets; it does not elaborate on parameter semantics beyond the schema. A baseline of 3 applies when the schema already carries that information.
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 clearly indicates a specific resource ('Requests over time for the authenticated account') and a specific data representation (zero-filled time buckets), which makes the tool's purpose obvious. It does not explicitly differentiate it from similar siblings such as 'usage_report' or 'usage_stats', but the resource and behavior are described well enough to infer a distinct purpose.
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 context for when to use the tool ('Same data as the dashboard chart and CSV export') but does not explicitly state when to use this tool versus alternative siblings like 'usage_report' or 'usage_stats'. The various window options give usage context, but there is no direct WANT claim about alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.