Recommends relevant design skills from sketch context or a free-text description, helping you choose techniques for generative art projects.
Recommend relevant design skills based on sketch context and/or free-text description
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | Free-text description of what you're working on (e.g., 'atmospheric landscape with watercolor') | |
| sketchId | No | ID of a loaded sketch to analyze for skill recommendations |
Changes observed during successful MCP inspections.
v0.5.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It does not state that the operation is read-only, whether it has side effects, how many recommendations are returned, or how results are ranked; 'recommend' implies non-mutation but only by inference.
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 front-loaded sentence with no redundant or filler content. It is appropriately sized for the amount of information it chooses to convey.
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 simple two-parameter recommendation tool this is close, but with no output schema and no annotations the description should say more about what is returned and how recommendations relate to load_skill. It is minimally adequate but leaves workflow and return-shape gaps.
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 both parameters are already fully documented. The description mentions context and sketch-based recommendation, aligning with the schema but adding no syntax, precedence, or combination semantics beyond it.
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 uses a specific verb and resource: recommend relevant design skills, qualified by sketch context and/or free-text description. It is clear on its own, but it does not distinguish itself from siblings like list_skills or explain the relationship to load_skill.
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?
There is no explicit guidance on when to call this rather than list_skills, nor any when-not conditions or workflow after receiving recommendations. The input conditions are stated, but they describe parameters rather than usage selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.