ontomics

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool does (checks naming conventions), what it returns (verdict with canonical form and suggestions), and its scope (detects specific inconsistencies). However, it doesn't mention potential limitations like supported programming languages or convention sources.

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 the tool's function and output, the second provides usage guidance. Every phrase adds value without redundancy, 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?

For a single-parameter tool with no annotations and no output schema, the description provides good coverage of purpose, usage, and behavior. It could be more complete by detailing the output format (e.g., structure of suggestions) or error handling, but it adequately supports tool selection and invocation.

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 schema description coverage is 100%, with the single parameter 'identifier' well-documented in the schema. The description adds minimal value beyond the schema by providing examples ('n_dims', 'numFeatures') that illustrate the parameter's purpose, but doesn't explain format constraints or edge cases.

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 ('check', 'detects', 'returns') and resources ('identifier', 'project naming conventions'). It distinguishes from siblings by focusing on naming convention validation rather than listing, describing, or suggesting names.

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 usage guidance with 'Use before committing new code or when reviewing identifier names,' giving clear context for when to apply this tool. It also distinguishes from alternatives like 'suggest_name' by focusing on validation rather than generation.

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