By name: mcp-audit description: Analyze MCP tool usage patterns, reward/time deltas conditioned on MCP adoption, and zero-MCP investigation. Triggers on mcp audit, mcp analysis, mcp impact, tool usage analysis, did mcp help. user-invocable: true
MCP Audit
Analyze MCP (Sourcegraph) tool usage across benchmark runs to understand where MCP helps, hurts, or goes unused.
What This Does
Runs scripts/mcp_audit.py which:
- Collects
task_metrics.jsonfrom paired_rerun batches (BL + SF on same VM) - Pairs baseline vs sourcegraph_full tasks for fair comparison
- Classifies tasks by MCP usage: zero-MCP vs used-MCP (light/moderate/heavy)
- Computes reward and time deltas conditioned on actual MCP usage
- Identifies negative flips (baseline pass → MCP fail)
Steps
1. Run the MCP audit
cd ~/CodeScaleBench && python3 scripts/mcp_audit.py --json --verbose 2>/dev/null
Save the JSON output for analysis. The script prints progress to stderr and results to stdout.
2. Parse and present key findings
From the JSON output, present these tables:
Overview:
| Metric | Value |
|---|---|
| Total unique tasks | N |
| Complete BL+SF pairs | N |
| Used-MCP tasks | N |
| Zero-MCP tasks | N |
| Total MCP calls | N |
Per-benchmark MCP adoption:
| Benchmark | Total | Used MCP | Zero MCP | Zero % |
|---|
Reward deltas (used-MCP only, cleaner signal):
| Group | N | BL Mean | SF Mean | Delta | p-value |
|---|---|---|---|---|---|
| Used-MCP | N | X | Y | +Z% | p |
| Zero-MCP | N | X | Y | -Z% | p |
| Light (1-5 calls) | N | X | Y | Z% | |
| Moderate (6-20) | N | X | Y | Z% | |
| Heavy (20+) | N | X | Y | Z% |
Timing deltas:
| Group | BL Mean (s) | SF Mean (s) | Delta |
|---|
3. Investigate zero-MCP tasks
For each zero-MCP task, classify the reason:
- Trivially local: Task requires only local file operations (e.g., DependEval dependency_recognition)
- Explicit file list: Instructions specify exact files to examine (e.g., CodeReview)
- Full local codebase: Complete codebase available in container (e.g., SWE-Perf)
- Both configs failed: Neither baseline nor SG_full succeeded
- Agent confusion: MCP available but agent didn't discover/use it (investigate transcript)
For unexplained zero-MCP cases, offer to read the transcript:
# Find the task's transcript
find $(readlink -f runs/official) -path "*sourcegraph_full*" -name "claude-code.txt" | xargs grep -l "TASK_ID_HERE" 2>/dev/null
4. Check for negative flips
List any tasks where baseline passes but SG_full fails (reward regression):
- In used-MCP group: Indicates MCP is actively harmful on these tasks
- In zero-MCP group: Indicates preamble overhead or non-determinism
5. MCP tool distribution
Show which MCP tools are most/least used:
| Tool | Calls | Tasks | Avg/Task |
|---|---|---|---|
| keyword_search | N | N | X |
| nls_search | N | N | X |
| read_file | N | N | X |
| ... |
6. Summary and recommendations
Synthesize findings into:
- MCP value: Where it demonstrably helps (search-heavy benchmarks)
- MCP risk: Where it hurts (implementation-heavy, preamble overhead)
- Optimization opportunities: Zero-MCP tasks that SHOULD use MCP but don't
- Cost-benefit: Is the token/time overhead justified by reward improvement?
Variants
All runs (not just paired reruns)
python3 scripts/mcp_audit.py --all-runs --json --verbose
Text output (human-readable)
python3 scripts/mcp_audit.py --verbose
Save to file
python3 scripts/mcp_audit.py --json --verbose --output docs/MCP_AUDIT_$(date +%Y-%m-%d).md
Key Technical Notes
- Transcript-first extraction: Tool counts come from
claude-code.txt(includes Task subagent MCP calls), NOTtrajectory.json(main-agent only). This was fixed in commit 59cdf7db. - Paired reruns: BL and SF run concurrently on same VM, eliminating load confounds. Prefixed
paired_rerun_*in runs/official/. - Valid task filter: Tasks with <10s agent time or 0 output tokens are excluded (auth failures).
- MCP tool name variants: Some batches use
sg_prefix (mcp__sourcegraph__sg_keyword_search), others don't. The script handles both. - Zero-MCP != MCP failure: Most zero-MCP tasks rationally chose local tools. Only investigate if the task type suggests MCP should help.
Related Skills
/compare-configs— Binary pass/fail divergence (simpler, doesn't condition on MCP usage)/evaluate-traces— Comprehensive trace audit (broader scope, includes data integrity)/cost-report— Token and cost analysis (doesn't pair tasks or condition on MCP)