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anilcancakir

skillsmp-mcp-server

by anilcancakir

AI Search Skills

skillsmp_ai_search
Read-onlyIdempotent

Search skills from the SkillsMP marketplace by describing your goal in natural language. Understands intent to find matching skills.

Instructions

Semantic search for skills using natural language queries. Powered by AI to understand intent.

Use this when keywords aren't enough - describe what you want to accomplish.

Parameters:

  • query: Natural language query (e.g., "How to build REST APIs with authentication")

Examples: "tools for web scraping", "help with React testing", "automate deployments"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language query for AI semantic search (e.g., 'How to create a web scraper', 'tools for SEO optimization')
response_formatNoOutput format: 'markdown' for human-readable or 'json' for machine-readablemarkdown
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, etc. Description adds the semantic search aspect but no additional behavioral details like pagination or result format. Adequate but not enhanced.

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?

Very concise: short paragraph, parameter list, and examples. No wasted words, well-organized for easy scanning.

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

Completeness4/5

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

For a search tool without output schema, description provides input guidelines, examples, and response_format parameter hint. Could mention return type (list of skills) but overall sufficient.

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 descriptions already cover both parameters (100% coverage). Description adds example queries which provide context but does not add significant new semantics beyond schema.

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 performs semantic search for skills using natural language queries. It distinguishes from sibling tools like skillsmp_search by emphasizing intent understanding and contrast with keyword search.

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?

Explicitly says 'Use this when keywords aren't enough' providing clear when-to-use guidance. Includes examples but does not explicitly name alternative tools.

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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