Search public Wikivibe AI-agent skills with GitHub metadata, project signals, install prompts and usage rules.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | No | Search phrase. Optional when category, stack or riskLevel is provided. | |
| stack | No | Technology or tool, for example Astro, MCP, Directus, Storybook. | |
| locale | No | ru | |
| category | No | Skill category slug, for example coding, search, data, automation, security, ai-ml, devops. | |
| riskLevel | No |
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 the content of results (GitHub metadata, etc.) but does not disclose read-only nature, auth requirements, rate limits, or any side effects. The transparency is minimal beyond what the name implies.
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 sentence that immediately conveys the main purpose and key data included. It is front-loaded with the action and resource, with no extraneous words or unnecessary 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?
The tool has 6 parameters with 50% schema coverage and no output schema. The description does not explain return format, pagination, filtering behavior, or parameter relationships. It is insufficient for a complete understanding.
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 description does not elaborate on any of the six parameters. With 50% schema description coverage, the description adds no additional semantics about how parameters interact or are used, failing to compensate for the gap.
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 action 'Search' and the resource 'public Wikivibe AI-agent skills', and specifies additional metadata included (GitHub metadata, project signals, install prompts, usage rules). This distinguishes it from sibling tools like search_articles or get_skill which handle different resources.
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 does not explicitly guide when to use this tool versus alternatives like search_articles. Usage is implied for searching skills, but no exclusion or alternative mention is provided, leading to an implied usage scenario.
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 targets a distinct resource and action: get for single items, list for collections, search for finding, and recommend for project-based suggestions. No two tools have overlapping purposes.
All tool names follow a consistent verb_noun snake_case pattern (e.g., get_article, search_skills). The verbs and nouns are clearly descriptive and uniform across the set.
With 9 tools, the set is well-scoped for a public knowledge server covering articles, design templates, and AI skills. Each tool serves a clear role without redundancy or overload.
The tool set provides comprehensive read and search operations for all three content types, plus a recommendation feature. Minor gaps exist, such as no listing of all articles by section or pagination support, but core workflows are covered.
