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anilcancakir

skillsmp-mcp-server

by anilcancakir

Install Skill

skillsmp_install_skill

Installs skills from a GitHub repository to AI coding agents. Requires source (owner/repo), skill names, and target agents.

Instructions

Install skills from GitHub to AI coding agents.

Parameters:

  • source: GitHub "owner/repo" (e.g., "anthropics/claude-code") [REQUIRED]

  • skills: Skill names, comma-separated (e.g., "frontend-design,backend-dev") [REQUIRED]

  • agents: Target agents, comma-separated [REQUIRED] Valid: claude-code, cursor, codex, opencode, antigravity, github-copilot, roo

  • global: Install user-level instead of project-level (default: false)

Example: source="anthropics/claude-code", skills="frontend-design", agents="claude-code"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYesGitHub shorthand 'owner/repo' (e.g., 'davila7/claude-code-templates'), full GitHub URL, or local path
skillsYesSkill names to install (comma-separated: 'skill1,skill2' or array). REQUIRED.
agentsYesTarget agents (comma-separated: 'claude-code,cursor'). Valid: opencode, claude-code, codex, cursor, antigravity, github-copilot, roo. REQUIRED.
globalNoInstall globally (user-level) instead of project-level (default: false)
Behavior3/5

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

Annotations already indicate readOnlyHint=false, so the description correctly implies a write operation. It adds details about installation scope (project vs global) but does not mention side effects like overwriting existing skills or required permissions.

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?

The description is concise with a front-loaded purpose, followed by parameter details and an example. Every sentence adds value without redundancy.

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 tool with 4 parameters and no output schema, the description provides adequate purpose, parameter details, and an example. It lacks information on return values or error handling, but is otherwise complete.

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 coverage is 100%, so baseline is 3. The description rephrases parameters and adds an example but does not significantly extend meaning beyond the schema descriptions (e.g., valid agents are listed in schema too).

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 'Install skills from GitHub to AI coding agents,' which is a specific verb-resource pair. It distinguishes from sibling tools like skillsmp_ai_search or skillsmp_get_skill_content by focusing on installation.

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

Usage Guidelines3/5

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

The description explains parameters and provides an example but does not explicitly state when to use this tool versus alternatives or when not to use it. Usage is implied but not formally guided.

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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