save_pattern

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 clearly states that the tool stores a pattern and returns a pattern_id, which informs the agent of the persistence side effect. However, it does not disclose whether saving a pattern with an existing name overwrites or errors, nor does it mention any permissions or constraints. This minor gap prevents a perfect score, but the core behavior is transparent.

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 appropriately sized, starting with a one-sentence purpose, then a helpful knowledge hierarchy, and a clear Args/Returns structure. Every sentence contributes value: the knowledge hierarchy explains when to use the tool, and the Args section explains parameters. No fluff or redundancy.

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 storage tool with four parameters, the description fully covers the operation, return value, and usage context. The knowledge hierarchy and sibling references provide integration context. The output schema is implied by the documented return value (pattern_id), so no additional explanation is needed. The tool is well-specified for an agent to invoke correctly.

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 coverage is 0%, so the description must compensate. It does so with a dedicated 'Args:' section explaining each parameter: name (with example), description (purpose), code (optional snippet), tags (retrieval purpose). This adds meaning well beyond the bare schema properties, giving the agent all necessary semantic information.

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 opens with a specific verb+resource: 'Store a reusable modeling pattern (code snippet or workflow) into the pattern memory.' It clearly indicates the action and the target. It also distinguishes itself from siblings by referencing recall_patterns and inspect_freecad in the knowledge hierarchy, making the tool's niche explicit.

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?

Explicit usage guidance is provided: 'Call this after a non-trivial approach worked.' The knowledge hierarchy states when to use alternatives ('recall_patterns when unsure', 'inspect_freecad for API details') and that successful new approaches should be stored back here. This gives the agent clear decision criteria for selecting this tool over others.

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