ai_generate_pattern

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 mentions using 'DeepSeek AI' but doesn't explain key behavioral traits: whether this is a read-only or mutating operation, potential rate limits, authentication needs, or what the output looks like (e.g., a pattern string or error handling). For an AI-based generation tool, this lack of detail is a significant gap.

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 a single, efficient sentence: 'Generate a Strudel pattern from natural language description using DeepSeek AI.' It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity. Every part of the sentence earns its place by specifying the action, resource, input type, and method.

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 complexity of an AI-based generation tool with no annotations and no output schema, the description is incomplete. It doesn't explain the return value (e.g., a Strudel pattern string or error messages), behavioral aspects like rate limits or costs, or how it differs from sibling tools. For a tool that likely involves external AI services, more context is needed to guide effective use.

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 input schema already documents all parameters (prompt, style, key, bpm) with clear descriptions. The tool description adds no additional parameter semantics beyond what's in the schema, such as examples or constraints. This meets the baseline of 3, as the schema does the heavy lifting, but the description doesn't compensate or enhance understanding.

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: 'Generate a Strudel pattern from natural language description using DeepSeek AI.' It specifies the verb ('generate'), resource ('Strudel pattern'), and method ('using DeepSeek AI'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'generate_pattern' or 'ai_enhance_pattern,' which prevents a perfect score.

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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools such as 'generate_pattern' (which might generate patterns without AI) or 'ai_enhance_pattern' (which might modify existing patterns), leaving the agent without context for selection. Usage is implied only through the tool's name and description, but no explicit when/when-not instructions are given.

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