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Structured design dialogue that validates ideas before implementation begins.
name: {server_name} description: Dynamic access to {server_name} MCP server ({tool_count} tools) version: 1.0.0 ---
Full skill instructions
This skill provides dynamic access to the {server_name} MCP server without loading all tool definitions into context.
Traditional MCP approach:
This skill approach:
Instead of loading all MCP tool definitions upfront, this skill:
{tool_list}
When the user's request matches this skill's capabilities:
Step 1: Identify the right tool from the list above
Step 2: ALWAYS get tool details first to obtain correct parameter names and types:
cd $SKILL_DIR
python executor.py --describe tool_name
This loads ONLY that tool's schema, not all tools.
Step 3: Generate a tool call using the exact parameter names from Step 2:
{{
"tool": "tool_name",
"arguments": {{
"param1": "value1",
"param2": "value2"
}}
}}
Step 4: Execute via bash:
cd $SKILL_DIR
python executor.py --call 'YOUR_JSON_HERE'
IMPORTANT: Replace $SKILL_DIR with the actual discovered path of this skill directory.
You MUST use --describe before calling any tool to get the correct parameter names and types. Do not guess parameter names as this will result in errors.
User: "Use {server_name} to do X"
Your workflow:
{first_tool_name}python executor.py --describe {first_tool_name}cd $SKILL_DIR
python executor.py --call '{{"tool": "{first_tool_name}", "arguments": {{"param1": "value"}}}}'
cd $SKILL_DIR
python executor.py --describe {first_tool_name}
Returns the full schema with parameter names, types, and requirements.
If the executor returns an error:
--describe to get the exact parameter namesCommon error: "Invalid arguments for tool" - This usually means you used an incorrect parameter name. Always run --describe first to get the correct parameter names.
Context usage comparison for this skill:
| Scenario | MCP (preload) | Skill (dynamic) |
|---|---|---|
| Idle | {estimated_tokens} tokens | 100 tokens |
| Active | {estimated_tokens} tokens | 5k tokens |
| Executing | {estimated_tokens} tokens | 0 tokens |
Savings: ~{savings_percentage}% reduction in typical usage
This skill was auto-generated from an MCP server configuration. Generator: mcp_to_skill.py
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