Token Optimization Mcp — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Token Optimization Mcp (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Production-ready Model Context Protocol server for token counting, prompt compression, model routing and semantic caching. Zero external API calls — works fully offline.
| Tool | Description |
|---|---|
estimate_tokens | Count tokens for any text+model (calibrated chars/token ratios) |
compress_prompt | Shrink prompts with trim, summarize_hint or aggressive strategy |
route_model | Pick cheapest model meeting quality + context requirements |
cache_lookup | Semantic cache hit/miss by prompt or pre-computed key |
cache_store | Store prompt+result with token-savings metadata |
cache_invalidate | Remove one or all cache entries |
analyze_context | Conversation health: role breakdown, issues, recommendations |
savings_report | Session-level token/USD savings dashboard |
deduplicate_messages | Remove duplicate turns, count saved tokens |
cd mcps/token-optimization-mcp
uv sync
# stdio – Claude Code / Copilot
uv run main.py
# SSE – LangGraph / CrewAI / browser
uv run main.py --sse --port 8001| Variable | Default | Description |
|---|---|---|
USE_REDIS | false | Enable Redis backend |
REDIS_URL | redis://localhost:6379/1 | Redis connection URL |
CACHE_TTL_SECONDS | 86400 | Default cache TTL (1 day) |
RATE_LIMIT_PER_MIN | 120 | Requests/min per client |
AUDIT_LOG_ENABLED | true | Print audit log to stdout |
~/.claude/settings.json){
"mcpServers": {
"token-optimization": {
"command": "uv",
"args": ["run", "/path/to/token-optimization-mcp/main.py"]
}
}
}.vscode/mcp.json){
"servers": {
"token-optimization": {
"type": "stdio",
"command": "uv",
"args": ["run", "${workspaceFolder}/mcps/token-optimization-mcp/main.py"]
}
}
}http://127.0.0.1:8001/sse| Model | Context | Quality | Cost/1k |
|---|---|---|---|
github:copilot | 128k | 8 | free |
gpt-4o-mini | 128k | 7 | $0.00015 |
claude-3-5-haiku | 200k | 7 | $0.00025 |
gemini-1.5-flash | 1M | 6 | $0.000075 |
gpt-4o | 128k | 9 | $0.005 |
claude-3-5-sonnet | 200k | 9 | $0.003 |
claude-3-opus | 200k | 10 | $0.015 |
uv run --group test pytest
# 118 tests, 100% coveragetoken-optimization-mcp/
├── main.py ← FastMCP server (9 tools)
├── pyproject.toml
├── README.md
├── tests/
│ ├── conftest.py ← state-reset fixtures
│ ├── test_helpers.py ← unit tests + Hypothesis
│ └── test_tools.py ← integration tests per tool
└── mcp-servers/
└── context-cache-server/ ← standalone Redis-backed sub-server
├── server.py
├── config.py
└── security.py~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.