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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so the trailing 'Read-only' sentence adds nothing new. The genuinely additive disclosure is scope and freshness: results are organization-wide and cover only the last 30 days, which tells the agent the data is not arbitrarily filterable and is not real-time. Return shape, refresh cadence, and any data-latency caveats are left unstated.

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?

Front-loaded with the verb and the metric list, then scope, then use case. Two sentences are low-value: 'measure their impact on productivity' is promotional framing rather than operational detail, and 'Read-only' merely restates readOnlyHint.

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 parameters, no output schema, and rich annotations, the main remaining burden is telling the agent what comes back, and the description does that via an explicit metric list plus a fixed time window and org-wide scope. It stops short of describing aggregation level, null/zero-data behavior, or whether historical comparison is possible, which keeps it from a 5.

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 tool takes zero parameters, so the schema has nothing to explain and the baseline is 4. The description usefully confirms there is no filter surface by declaring the output is organization-wide and fixed to a 30-day window, which closes the natural 'can I narrow this?' question.

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 names a specific verb ('Get') and resource ('AI coding tool adoption metrics') and enumerates the concrete metrics returned: Copilot acceptance rate, Cursor active users, AI-generated code percentage, and suggestions per developer. It does not name the sibling trend tool as an alternative, but the explicit 'last 30 days' fixed window does implicitly distinguish it from a time-series variant.

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?

It gives a use context ('understand how the team is using AI coding assistants and measure their impact on productivity'), which implies when to reach for it. However, it never states when not to use it or points to the alternative for trends over time, so an agent must infer the boundary from sibling names alone.

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