get_repository_stats

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 describes what the tool calculates (e.g., statistics like total stars, language breakdown) but lacks details on performance aspects (e.g., computation time, data freshness), error handling, or output format. The mention of 'use_cache' in the schema hints at caching behavior, but the description does not elaborate on this or other operational traits.

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 sized and front-loaded, starting with the core function and listing key statistics in a clear, bullet-like format. Every sentence adds value by specifying the tool's output and use case without redundancy or unnecessary details, making it efficient and easy to understand.

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 complexity (aggregating statistics across all repositories) and the absence of both annotations and an output schema, the description is somewhat incomplete. It outlines what statistics are calculated but does not specify the return structure, data granularity, or potential limitations (e.g., handling of large datasets). This leaves gaps for an AI agent to understand the full behavioral context.

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 1 parameter with 100% description coverage, so the schema already documents it well. The description does not add any parameter-specific information beyond what the schema provides, but since there is only one parameter and schema coverage is high, the baseline is strong. No additional semantic context is needed, but the description could have explained the impact of caching on the statistics.

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 with a specific verb ('calculates') and resource ('aggregate statistics across all repositories'), and it distinguishes itself from siblings by focusing on comprehensive analytics rather than listing, searching, or detailing individual repositories. It explicitly lists the types of statistics included, such as total count, stars, forks, language breakdown, and recent updates.

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 clear context for when to use this tool ('Perfect for portfolio summaries and analytics'), which helps differentiate it from siblings like list_repositories or get_repository_details. However, it does not explicitly state when not to use it or name specific alternatives for overlapping functions, such as generate_portfolio_summary, which might serve a similar purpose.

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