ontomics

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 effectively describes what the tool does (lists naming conventions with real examples) and its accuracy advantage ('more accurate than guessing from a few files'), which are key behavioral traits. However, it doesn't mention potential limitations like performance or data freshness, leaving some gaps in transparency.

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 front-loaded with the core purpose in the first sentence, followed by specific details and usage guidelines. Every sentence adds value: the first defines the tool, the second elaborates on what it lists, the third highlights accuracy, and the fourth provides usage contexts. There is no wasted text, making it highly efficient and well-structured.

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 (listing naming conventions with examples) and the absence of annotations and output schema, the description does a good job of explaining what the tool does and when to use it. However, it doesn't describe the output format or potential errors, which could leave the agent uncertain about how to interpret results. This minor gap prevents a perfect score.

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 tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's purpose and usage. A baseline of 4 is applied since no parameters exist, and the description doesn't attempt to explain non-existent parameters.

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: 'List the project's actual naming conventions detected from code' with specific details about what it lists (prefix patterns, suffix patterns, conversion patterns, casing rules) and includes real examples. It distinguishes from siblings by emphasizing 'actual naming conventions detected from code' and 'more accurate than guessing from a few files', which differentiates it from tools like 'check_naming' or 'suggest_name'.

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 explicitly states when to use this tool: 'Use when asked about coding style, before writing new code, or when onboarding to a project.' This provides clear, actionable guidance on appropriate contexts for invoking the tool, helping the agent decide when to select it over alternatives.

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