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loaditout-mcp-server

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install_skill

Retrieve the configuration JSON needed to install a single AI agent skill for your platform. Returns config path, JSON config, and setup instructions.

Instructions

Get the platform-specific configuration JSON needed to install a single skill. Returns a JSON object with config_path (where to write the config), config (the JSON to write), and instructions (human-readable setup steps). This tool is read-only and does NOT write any files. Use this when the user wants to install a specific skill and you need the exact config for their agent platform. Do not use this for batch installs (use install_batch instead) or for packs (use install_pack instead). The skill slug is automatically saved to agent memory after calling this tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesSkill slug in owner/repo format. Examples: 'supabase/mcp', 'microsoft/playwright-mcp'. Must be a valid slug from the Loaditout registry.
agentYesThe target agent platform. Determines the config file format and path. Use 'claude-code' for Claude Code (.claude/settings.json), 'cursor' for Cursor (.cursor/mcp.json), 'generic' if the platform is unknown.
Behavior4/5

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

No annotations provided, so description carries full burden. States the tool is read-only ('does NOT write any files') and mentions memory side effect. Could add more on required permissions or error conditions, but adequately describes core behaviors.

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?

Four sentences, front-loaded with purpose and return format. Some redundancy (e.g., 'This tool is read-only' repeated from context). Could be tightened but overall efficient.

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?

No output schema, so description compensates by detailing return object fields. Covers side effects (memory save) and limitations. Missing error handling or prerequisites (e.g., authentication), but acceptable given tool simplicity.

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

Parameters4/5

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

Schema coverage is 100% with descriptions. Description adds context about return format (config_path, config, instructions) and confirms slug format (owner/repo). Also provides insight into how agent enum values map to platforms (e.g., 'claude-code' for .claude/settings.json), adding value 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?

Clearly states 'Get the platform-specific configuration JSON needed to install a single skill', with a specific verb (get/config) and resource (config JSON for a skill). Distinguishes from siblings by mentioning batch installs and packs.

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?

Explicitly says 'Use this when the user wants to install a specific skill...' and warns 'Do not use this for batch installs (use install_batch instead) or for packs (use install_pack instead)'. Also notes that the skill slug is automatically saved to agent memory.

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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