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agentrix-ai

Uno MCP Stdio

by agentrix-ai

uno_skills_search

Semantically search thousands of Cursor Agent Skills to discover relevant capabilities, guides, or workflows, returning skill metadata and quality signals for selection.

Instructions

从数千个 Cursor Agent Skills 中语义搜索最相关的 skills。 返回 skill 的 meta 信息 + 质量信号,供选择参考。 使用场景:当你需要一个「能力/指南/工作流」而不是单个工具时。 搜索后可用 uno_skills_fetch 获取选中 skill 的完整 SKILL.md 内容。

返回字段说明:

  • skill_id: 唯一标识,用于 uno_skills_fetch

  • skill_name: 名称

  • description: 描述(可能含 en/zh)

  • categories: 分类标签

  • stars: GitHub stars(流行度)

  • forks: 被 fork 次数(流行度)

  • token_count: SKILL.md 的 token 数(加载成本参考)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo返回数量,默认 20,最大 20
queryYes自然语言描述你的需求(如:'抓取微信公众号文章')
categoryNo按分类过滤(可选,如:工具、代码、文档、API)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does reasonably well: it discloses the return shape (meta + quality signals) and enumerates every returned field including stars/forks/token_count as quality signals. It omits whether the operation is read-only, result ordering/ranking behavior, and pagination, which are gaps but minor for a search tool.

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

Conciseness4/5

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

Well-structured and front-loaded: purpose, usage scenario, and next-step routing come first, followed by a scannable field-by-field return list. The field enumeration is slightly verbose but each entry earns its place by explaining meaning.

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?

There is no output schema, and the description compensates by explaining every returned field and the search→fetch workflow. Combined with the usage scenario and alternatives, an agent has everything needed to call it correctly.

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?

Schema description coverage is 100%, so the schema already documents query, limit (default 20, max 20) and category. The description adds no syntax or format detail beyond what the schema provides, so the baseline 3 applies.

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 states a specific verb and resource: 语义搜索 (semantic search) over Cursor Agent Skills, from thousands of entries. It clearly distinguishes itself from siblings by noting it returns meta/quality signals and that uno_skills_fetch retrieves full content, so an agent can tell it apart from uno_search_tools and uno_skills_fetch.

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

Usage Guidelines4/5

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

It gives an explicit usage scenario (当你需要一个「能力/指南/工作流」而不是单个工具时) and names the follow-up tool uno_skills_fetch for retrieving full content. It implies the alternative for single tools without naming uno_search_tools explicitly, so it stops just short of full when-not guidance.

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