benchmark_analyze_preferences

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

No annotations are provided, so the description carries the full burden. It identifies the data source ('battle voting history') and analysis dimensions, but fails to disclose whether this is a read-only operation, what aggregation or statistical methods are used, what output format to expect, or any side effects. The agent is left guessing about core runtime behavior.

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?

A single, front-loaded sentence that conveys the core function without filler. It clearly states the verb, object, and key dimensions, making it easy to scan and parse. No wasted words.

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?

There is no output schema, yet the description does not describe the return value at all. It also omits details like how preferences are quantified, what the output structure is, and how the two optional parameters alter behavior. For an analysis tool, this is a significant completeness gap.

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 description coverage is 100%, so the schema already documents 'category' and 'min_votes'. The description adds no extra meaning beyond what the schema provides, such as how the category filter interacts with preferences or what min_votes affects. Baseline 3 is appropriate given the full 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 uses a specific verb ('Analyze') and names a distinct resource: 'empirical user preference patterns (conciseness, structure, tone) from battle voting history.' This clearly differentiates it from sibling tools like benchmark_get_results, benchmark_get_leaderboard, and benchmark_compare_models.

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 states what the tool does but gives no guidance on when to choose it over alternatives. It does not mention exclusions, prerequisites, or which sibling tools to use for other analysis purposes. The intended context is implied but not explicitly spelled out.

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