get_agent_edits

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this is a 'Get' operation (implying read-only) and describes the metrics returned, but doesn't cover important aspects like authentication requirements, rate limits, pagination, error conditions, or data freshness. For a tool with no annotation coverage, this leaves significant behavioral gaps.

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 appropriately concise with two sentences. The first sentence clearly states the purpose and metrics, while the second adds valuable context about team effectiveness. There's no wasted language, and the information is front-loaded. It could potentially be improved with more structured guidance, but it's efficient as-is.

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 the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It explains what metrics are returned and their purpose, but lacks details about the return format, data aggregation, or how the metrics are calculated. Without an output schema, more information about the response structure would be helpful for a complete understanding.

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 description doesn't mention any parameters, while the input schema has 100% description coverage with clear documentation of 'startDate', 'endDate', and 'users'. Since schema coverage is high (>80%), the baseline score is 3. The description adds no parameter semantics beyond what's already in the schema, but doesn't need to compensate for poor schema documentation.

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's purpose: 'Get agent edit metrics: suggested vs accepted vs rejected diffs and lines.' It specifies the verb ('Get') and resource ('agent edit metrics'), and provides meaningful context about what the metrics measure ('how effectively the team is using AI-generated code'). However, it doesn't explicitly differentiate from sibling tools like 'get_usage_events' or 'get_user_deep_dive' that might also provide related 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. It mentions the metrics show team effectiveness with AI-generated code, but doesn't specify use cases, prerequisites, or exclusions. With many sibling tools (e.g., 'get_daily_usage', 'get_user_deep_dive'), there's no indication of how this tool differs in scope or when it's the appropriate choice.

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