ontomics

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

With no annotations provided, the description carries full burden. It explains the tool uses project conventions (prefixes, patterns, conversion patterns) to generate consistent names, which adds useful behavioral context beyond basic functionality. However, it doesn't disclose potential limitations like character constraints, language-specific rules, or error handling.

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 how it works, the second provides usage guidance. Every element adds value with zero 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?

For a single-parameter tool with no annotations and no output schema, the description provides good coverage of purpose, usage context, and behavioral approach. The main gap is lack of information about return values or output format, which would be helpful given no output schema exists.

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 the single 'description' parameter. The description adds marginal value by explaining this is a 'natural language description' and providing an example ('e.g., count of features'), but doesn't significantly expand beyond what the schema provides.

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 ('Generate', 'suggest') and resources ('project-consistent identifier names', 'natural language description'). It distinguishes from siblings by focusing on name generation rather than checking, describing, or listing concepts.

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: 'Use when naming new functions, variables, or parameters.' This gives clear context for when to invoke this tool versus alternatives like check_naming or list_conventions.

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