Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/consiliency/code-index-mcp/tool-statsgit clone --depth 1 https://github.com/Consiliency/Code-Index-MCPWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00397 |
| Opus 5 | $0.00000 | $0.00198 |
| Sonnet 5 | $0.00000 | $0.00079 |
| Haiku 4.5 | $0.00000 | $0.00040 |
Grade A, and why
tool-stats scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Tool Usage Statistics
Display statistics about tool usage patterns in the current session.
Usage
/tool-stats
What It Shows
This command analyzes your current session and displays:
-
Tool Usage Counts
- MCP tools used
- Native search tools used
- File operations
-
Performance Metrics
- Average response times
- Total time saved by using MCP
- Efficiency rating
-
MCP Compliance
- Percentage of searches using MCP
- Any violations of MCP-first strategy
- Suggestions for improvement
Example Output
📊 Tool Usage Statistics
========================
Session Duration: 15 minutes
Total Tool Calls: 42
MCP Tools (Recommended):
- symbol_lookup: 8 calls (19%)
- search_code: 12 calls (29%)
- get_status: 2 calls (5%)
Total MCP: 22 calls (52%)
Native Tools:
- Read: 18 calls (43%)
- Grep: 0 calls (0%) ✅
- Find: 0 calls (0%) ✅
- Glob: 2 calls (5%) ⚠️
Performance Impact:
- Time with MCP: 11.5s
- Time without MCP: ~680s
- Efficiency Gain: 59x faster
MCP-First Compliance: 100% ✅
All searches used MCP tools first!
Recommendations:
- Consider using search_code instead of Glob for file discovery
- Excellent MCP adoption rate!
Metrics Explained
- MCP Usage Rate: Percentage of search operations using MCP
- Efficiency Gain: How much faster than traditional search
- Compliance Score: Whether MCP tools are used before native search
Goal Metrics
Aim for:
- MCP Usage: >80% of searches
- Zero grep/find for content search
- Compliance: 100% MCP-first
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 74 lines · 0 tokens per session scan A b5d6875e417d
tool-stats is a command published in the GitHub repository Consiliency/Code-Index-MCP (57 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 397 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.