recommend_skillGiven a natural-language task, return the top 3 matching skills with the exact install command.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | What you want to do, e.g. "write a cold outreach email sequence". |
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 discloses that the tool returns top 3 matching skills with exact install commands, which is behaviorally transparent. It could mention lack of side effects or authorization needs, but the core behavior is clear.
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 concise sentence that wastes no words. It is front-loaded with the core purpose and output details.
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?
With no output schema, the description adequately explains the return structure (top 3 matching skills with install commands). It provides sufficient context for a simple one-parameter tool, though it could mention edge cases like no matches found.
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 already provides 100% coverage with a clear description and example for the only parameter 'task'. The tool description does not add significant semantics beyond what the schema offers, so a baseline score of 3 is appropriate.
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 verb 'recommend' and resource 'skills', and specifies the output format as top 3 matching skills with install commands. This distinguishes it from sibling tools like list_skills (lists all) and search_skills (search by name).
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 implies use when you have a natural-language task and need relevant skills. However, it does not explicitly state when not to use this tool or name alternative tools for different scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool serves a distinct purpose: retrieving sets, skills, categories, sets list, skill list, recommendation, and search. No overlapping functionality.
All tools use consistent snake_case with verb_noun pattern (e.g., get_set, list_categories, search_skills). No mixing of styles.
Seven tools is well-scoped for a skills directory, covering browsing, search, recommendation, and detailed retrieval without overburdening the agent.
Covers core operations: listing categories, skills, sets; searching; recommending; retrieving details. Minor gap: no direct category detail tool, but list_categories provides counts.