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. It discloses the tool's behavior by stating it returns an overall score plus top inconsistencies and uncovered identifiers, which adds useful context beyond the basic purpose. However, it lacks details on computational limits, performance characteristics, or error conditions that would be helpful for a quality analysis 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 in two sentences: the first explains what the tool does and what it returns, the second provides usage guidelines and additional return details. Every sentence adds value with zero wasted words, making it easy to parse 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's complexity (analyzing codebase vocabulary) and lack of annotations/output schema, the description does a good job explaining the purpose, usage, and return values. It could be more complete by specifying what 'this codebase' refers to contextually or detailing the scoring methodology, but it covers the essentials well for a tool with no parameters.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on the tool's function rather than inputs, earning a high score since it doesn't need to compensate for schema gaps.

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 specific verbs ('measure vocabulary health') and resources ('this codebase'), and distinguishes it from siblings by focusing on vocabulary analysis rather than naming checks, concept mapping, or other code quality aspects. It explicitly lists the three metrics returned: convention coverage, consistency ratio, and cluster cohesion.

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 explicit guidance on when to use this tool: 'when asked about code quality, naming consistency, or vocabulary health.' This directly addresses usage scenarios and distinguishes it from alternatives like check_naming or list_conventions by focusing on holistic vocabulary health assessment.

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