export_evolution_data

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

No annotations are provided, so the description must carry the burden. It explains the tool analyzes the last N weeks and returns per-dimension scores plus trends, which is useful behavioral context. However, it does not explicitly state whether the operation is read-only, has side effects, or requires any permissions.

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 three sentences long, front-loaded with the main purpose, and every sentence adds value. It is concise, well-structured, and free of redundant phrasing.

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?

The output schema exists, so return values are documented. The description covers the tool's purpose, behavior, and intended use case (feeding the dashboard). Given the tool's moderate complexity and available structured data, the description is complete.

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 input schema has 100% coverage with clear descriptions for all three parameters (repo, weeks, username). The description adds a slight clarification by mentioning 'last N weeks', which maps to the weeks parameter, but this does not go beyond the schema's existing descriptions.

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 exports Karpathy skill evolution data for Spider Chart visualization. It specifies the resource (evolution data), the verb (Export), and adds scope with weekly analysis and trend detection, distinguishing it from siblings like get_contribution_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 implies usage for generating spider chart visualizations and feeding the Next.js dashboard, but it does not explicitly state when to use this vs alternatives or mention any exclusions. It lacks direct comparison to sibling tools.

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