mcp-to-skills

name	mcp-to-skills
description	Use when the user wants to convert expensive MCP tools into on-demand Claude Code skills

Use This Skill When

• The user has a large MCP server but only uses a few tools
• The user wants zero idle token cost until a tool is invoked
• The user asks to convert an MCP server command into local Claude Code skills

Purpose

Read tool schemas from an MCP server and convert each tool into an independent Claude Code skill. Converted skills are written under .claude/skills/{service}-{tool-name}/SKILL.md and are discovered automatically by Claude when the user asks for that workflow.

Inputs

Infer the MCP server command from the user's request. The explicit command equivalent is /mcp-optimizer:mcp-to-skills <server-command>.

Example: /mcp-optimizer:mcp-to-skills npx @linear/mcp-server

Step 1: Extract MCP server schemas

python3 "${CLAUDE_SKILL_DIR}/scripts/mcp_inspect.py" --server "<resolved server command from the user's request>"

This returns the full tool list and each tool's inputSchema.

Step 2: Analyze tools and determine execution mode

For each tool:

1. Purpose analysis: determine the tool's role from its description and inputSchema

2. Execution mode selection:

   • Proxy mode (default): one-shot MCP server call via mcp_call.py
   • Native mode: for well-known APIs (GitHub, Linear, etc.), generate skills that call the API directly

   Condition	Mode
   Default / unknown API	Proxy
   Well-known service + official REST API	Native
   Local resource access (files, DB, etc.)	Proxy only

Step 3: Generate SKILL.md files

Generate a .claude/skills/{service}-{tool-name}/SKILL.md file for each tool.

Proxy mode SKILL.md template:

---
name: {service}-{tool-name}
description: {tool description}
---

Infer the required parameters from the user's request.

## Parameters

{parameter list and descriptions extracted from inputSchema}

## Execution

\`\`\`bash
python3 "{resolved absolute path to plugin mcp_call.py}" \
  --server "{server command}" \
  --tool "{tool_name}" \
  --args '{parameter JSON}'
\`\`\`

## Output

Extract key data from the response and present in a user-friendly format.
{tool-specific output formatting guidance}

Generation rules:

• Extract service name from MCP server name (e.g., @linear/mcp-server -> linear)
• Convert tool names from snake_case to kebab-case (e.g., list_issues -> list-issues)
• Keep descriptions in English
• Design skills to accept natural-language requests without relying on slash-command variables
• Resolve the plugin helper path during generation and write the concrete absolute path to mcp_call.py into each generated local skill
• Do not leave ${CLAUDE_SKILL_DIR} or ${CLAUDE_PLUGIN_ROOT} placeholders inside generated local skills

Step 4: Report results

Display the generated skill list to the user:

{N} skills generated successfully!

| Skill Directory | Description | Mode |
|-----------------|-------------|------|
| .claude/skills/{service}-{tool} | {description} | Proxy/Native |

Example natural-language requests:
  - List open issues for the Engineering team in Linear
  - Create a GitHub issue titled "Broken deploy"

Notes

• Running mcp_inspect.py starts the MCP server, so required environment variables (API keys, etc.) must be set
• Timeouts: 30s for schema extraction, 30s for tool calls
• Verify that the mcp_call.py path in generated skills is correct