generate_ranking_report

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

With no annotations provided, the description carries full burden. It discloses key behavioral traits: it generates a file (saves to reports/ranking.md), reads from a specific data source (data/scores.json), and produces a structured output. However, it doesn't mention error handling, file overwriting behavior, or performance characteristics like execution time or resource usage, leaving gaps for a mutation tool.

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 efficiently structured with a clear opening sentence stating the core action, followed by brief context on data source and report content, and ending with explicit sections for Args and Returns. Every sentence adds value without redundancy, making it easy to scan and understand quickly.

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 has one parameter with good description coverage and an output schema (Returns section details the result dict structure), the description is largely complete. It covers purpose, basic behavior, parameter meaning, and output format. Minor gaps include lack of error cases or dependencies on the data/scores.json file format, but overall it provides sufficient context for effective use.

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 adds meaningful context for the single parameter 'top_n' by explaining it controls 'Number of top entries to highlight' with a default value. Since schema description coverage is 0% (the schema only provides title and type), the description fully compensates by clarifying the parameter's purpose and default, though it could specify constraints like minimum/maximum values.

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 specific action ('Generate a Markdown ranking report and save to reports/ranking.md'), identifies the input source ('Reads scoring results from data/scores.json'), and specifies the output content ('overall ranking, per-track ranking, and individual evaluation summaries'). This distinguishes it from sibling tools like get_scoring_rubric or list_submissions which retrieve rather than generate reports.

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 context by mentioning it reads from data/scores.json, suggesting it should be used after scoring data is available. However, it doesn't explicitly state when to use this tool versus alternatives like save_scores or get_submission_detail, nor does it provide exclusion criteria or prerequisites beyond the implied data dependency.

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