| name | mcp-audit |
| description | Use when the user wants to measure MCP token waste, rank high-cost servers, or choose between project scoping and skill conversion |
Use This Skill When
- The user asks which MCP servers are wasting tokens
- The user wants evidence before removing or converting servers
- The user asks whether to keep MCP, scope it per project, or convert tools to skills
Purpose
Analyze the user's Claude Code session history (JSONL) to:
- Identify per-MCP-server tool usage frequency
- Estimate token waste from idle tool schemas
- Recommend skill conversion priorities
Inputs
Infer scope from the user's request. Mirror the explicit command /mcp-optimizer:mcp-audit. Default is project.
--scope project : analyze sessions for the current project only--scope all : analyze sessions across all projects
Step 1: Delegate analysis to a sub-agent
Session data can be large, so always use the Agent tool to delegate analysis and report generation. Only the final report text should remain in the main context.
Use the following prompt when invoking the Agent tool:
Perform an MCP token waste analysis.
1. Run the following command to get the analysis JSON:
python3 "${CLAUDE_SKILL_DIR}/scripts/session_analyzer.py" --scope <scope>
(Actual path for ${CLAUDE_SKILL_DIR}: <absolute path to this skill directory>)
2. Format the JSON result into the report template below and return it:
MCP Token Waste Analysis Report
================================
Scope: {scope} ({sessions_analyzed} sessions, {date_range})
| MCP Server | Tools | Used | Est. Wasted Tokens | Cost/Session | Priority |
|------------|-------|------|--------------------|-------------|----------|
| {name} | {total} | {used} ({ratio}%) | ~{waste} | {per_session} | {priority} |
Total estimated waste: ~{total_waste} tokens
Recommended Actions:
(list each item from the recommendations array, ranked by priority)
Unmatched Tools:
(show unmatched_mcp_tools if any exist)
Report rules:
- Priority indicators: HIGH = RED, MEDIUM = YELLOW, LOW = GREEN
- Use thousand separators for token counts (e.g., 126,000)
- Show ratios as percentages
- If 0 sessions found, output "No session data available for analysis"
- Include convert_command from recommendations
- Return only the final report text. Do NOT return the raw JSON.
Step 2: Display report
Output the report returned by the sub-agent directly to the user.
Step 3: Optimization paths
If the report contains recommended actions, present two optimization paths:
Option A: /mcp-optimizer:mcp-optimize — Keep MCP, reduce scope
- Best when: some servers are useful in this project but not all are needed
- Creates a project-local
.mcp.json with only relevant servers
- Fully reversible (delete
.mcp.json to revert)
- Suggest this first for users who want minimal disruption
Option B: /mcp-optimizer:mcp-to-skills — Convert to on-demand skills
- Best when: a server has many tools but only a few are used
- Converts individual MCP tools into slash-command skills
- Skills load only on invocation (zero idle token cost)
- Suggest this for high-waste servers with low usage ratios
Present the options like this:
- Explain each recommended server and its estimated savings
- For each server, suggest which path fits better based on usage patterns:
- High tool count + low usage ratio →
/mcp-optimizer:mcp-to-skills
- Server rarely needed in this project →
/mcp-optimizer:mcp-optimize
- Ask: "Which approach would you like? [optimize/convert/both]"
- If "optimize": invoke
/mcp-optimizer:mcp-optimize
- If "convert": invoke
/mcp-optimizer:mcp-to-skills for the recommended servers
- If "both": run
/mcp-optimizer:mcp-optimize first, then /mcp-optimizer:mcp-to-skills for remaining high-waste servers
Notes
- Session JSONL format may vary across Claude Code versions
- Token estimates are approximate (~120 tokens per tool schema)
- Actual savings depend on usage patterns
