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popular_skills

Return top N AI agent skills ranked by download count. Use for discovery or onboarding when user has no specific task in mind (e.g. "show me popular skills", "what can I do with this"). Do NOT use when user describes a specific task — use search_skills instead. Returns: slug, name, description, category, downloads, stars. On database error returns empty list — do not retry. Default limit 20, max 50. Follow up with get_skill only if user requests details on a specific result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many top skills to return. Default: 20. Max: 50. Use 5-10 for quick recommendations, 20-50 for browsing.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / limit / description
      Previous value: -"Number of top skills to return. Default: 20. Max: 50."New value: +"How many top skills to return. Default: 20. Max: 50. Use 5-10 for quick recommendations, 20-50 for browsing."
  2. Changed1 schema field changed
    • changedInput schema / properties / limit / description
      Previous value: -"Max results (1-50, default 20)"New value: +"Number of top skills to return. Default: 20. Max: 50."
  3. First observed

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full disclosure burden. It reveals that database errors return an empty list and instructs not to retry, documents the exact return fields (slug, name, description, category, downloads, stars), and specifies default and max limits. This is non-obvious, valuable behavioral context that goes beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is front-loaded with purpose, followed by essential guidance on when to use, what it returns, error handling, and follow-up actions. Every sentence contributes actionable information without waste, maintaining a clear structure.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a simple tool with one optional parameter and no output schema, the description covers return values, default/max, error behavior, and use cases, making it fully self-contained. It even specifies when to escalate to other tools, ensuring the agent has all context needed for correct invocation and interpretation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already fully documents the single optional parameter (limit) including default, max, and usage guidance (5-10 for quick recommendations, 20-50 for browsing). The description merely restates the default/max, adding no additional semantics, 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.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns top N AI agent skills ranked by download count, using a specific verb and resource. It explicitly distinguishes itself from search_skills and get_skill, eliminating ambiguity among sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit use cases ('discovery or onboarding when user has no specific task in mind') with example queries, and explicitly tells when NOT to use it (specific task -> search_skills). Also advises following up with get_skill only when details are requested, offering clear decision boundaries.

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

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