recommend

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 mentions that results are ranked with relevance scores and install guidance, but does not disclose any potential side effects, authentication needs, or limitations. This is adequate but not comprehensive.

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 three sentences: purpose with scope, usage guidance, and output format. It is front-loaded with essential information and contains no unnecessary 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?

Given the tool's recommendation nature, 4 parameters, and no output schema, the description sufficiently covers what the tool does and what it returns. It could mention pagination or sorting behavior, but the current description is adequate for an AI agent.

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 explains all parameters. The description adds some nuance to the 'task' parameter (e.g., natural language, technical question, prompt), which is helpful but not essential. Overall, the description adds marginal value beyond the schema.

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 recommends AI tools, skills, MCP servers, agents, rules, and resources from the SkillHub ecosystem based on user's task. It distinguishes itself from sibling tools like search and get_resource by focusing on discovery and ranking.

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 explicitly states when to use: when the user could benefit from discovering relevant AI tools, needs help finding the right framework/library, or is working on a task improvable with AI resources. It does not explicitly state when not to use, but the context is clear enough.

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