get_contribution_metrics

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

The description discloses that the operation is a retrieval and that returns are aggregated, which is helpful. However, there is no annotation context and the description does not mention authentication, rate limits, pagination, or potential side effects. The use of 'raw' followed by 'aggregated data' also introduces ambiguity about the true nature of the output.

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 compact: two sentences that state purpose, return content, and use case. It is front-loaded with the verb and resource, and no sentence is wasted. The minor 'raw/aggregated' tension is a semantic concern, not a structural one.

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 an output schema present and full parameter documentation in the schema, the description provides sufficient context for basic use. It names the key data categories and the primary use case. It lacks explicit alternative tool references and does not clarify the 'raw' vs 'aggregated' distinction, but the presence of a robust schema compensates.

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%, so the parameters are already well-documented in the input schema. The description does add a general 'filtered by time period' clue, but it does not provide additional meaning beyond what the schema already gives for each parameter. This meets the baseline for high schema coverage.

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 opens with a clear verb ('Retrieve'), a specific resource ('raw GitHub contribution metrics'), and a scoping mechanism ('filtered by time period'). It immediately distinguishes this from sibling tools like generate_weekly_impact_summary or analyze_karpathy_alignment by emphasizing raw retrieval and specific metric categories (commits, PRs, reviews, code changes).

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 phrase 'Useful for understanding contribution volume and patterns' provides clear context on when to use the tool. However, it does not explicitly mention when not to use it or point to alternative sibling tools, so it falls short of the highest bar.

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