Benchmark stats from 239 real landing pages

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

With no annotations, the description carries the full burden. It discloses that data is 'static local data, no network', indicating fast, non-networked, read-only behavior. It doesn't mention auth or side effects, which are not needed for a static data query.

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?

Two sentences: the first lists what is returned, the second gives usage guidance and a behavioral note. No redundant information; front-loaded with key output details.

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 zero parameters and no output schema, the description adequately covers the tool's output: it enumerates the main components (distribution stats, tell counts, low-scoring domains, score distribution). The behavioral note adds completeness.

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?

No parameters exist in the schema, so the description naturally does not need to add parameter meaning. However, it effectively adds value by detailing the output content (reference distribution, tell frequencies, etc.), compensating for the lack of an output schema.

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 explicitly states the tool returns reference distribution, tell frequencies, lowest-scoring domains, and score distribution for grade_landing_copy. It clearly distinguishes from sibling tools (check_ai_slop, grade_landing_copy) by focusing on benchmarking statistics.

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 gives explicit usage context: 'Use it to tell a user whether their score is actually good.' It implies the tool is for interpreting scores from grade_landing_copy, though it doesn't explicitly exclude other uses or mention alternatives.

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