get_agent_edits

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It only states 'gets metrics' but does not explain aggregation level (per user or team), data freshness, whether it's read-only, or how date ranges interact. For a tool with 3 parameters, critical behaviors like default aggregation (e.g., last 30 days, all users) are omitted.

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 two concise sentences with a clear subject-action-result structure. It front-loads the key information (what metrics) and adds context (why it matters). It could be slightly tighter by removing redundancy (e.g., 'shows how effectively' is implied by 'metrics'), but overall efficient.

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 no output schema and no annotations, the description should fully explain behavior and return values. It partially does for output (mentions diffs and lines) but lacks details on format (e.g., per user, totals), pagination, or error cases. Completeness is adequate for a simple metric tool but leaves notable gaps for agent invocation.

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 coverage is 100% with each parameter having a description (e.g., date formats, defaults). The description adds no extra meaning beyond the schema, so it meets the baseline of 3. It does not explain how parameters filter results or combine (e.g., users filter applies to all dates).

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 states the tool retrieves agent edit metrics (suggested, accepted, rejected diffs and lines) and explains its purpose (showing team effectiveness with AI-generated code). This verb-specific resource definition distinguishes it from sibling tools like get_usage_events or get_daily_usage, which focus on different metrics.

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 no guidance on when to use this tool versus alternatives like search or other get_* tools. It does not specify prerequisites, exclusions, or context like 'use this for aggregated metrics, use search for detailed breakdowns' – leaving the agent without decision support.

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