Token Optimization

Production-ready Model Context Protocol server for token counting, prompt compression, model routing and semantic caching. Zero external API calls — works fully offline.

Tools

Quick Start

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

Environment Variables

Registration

Claude Code (~/.claude/settings.json)

{
  "mcpServers": {
    "token-optimization": {
      "command": "uv",
      "args": ["run", "/path/to/token-optimization-mcp/main.py"]
    }
  }
}

VS Code Copilot (.vscode/mcp.json)

{
  "servers": {
    "token-optimization": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "${workspaceFolder}/mcps/token-optimization-mcp/main.py"]
    }
  }
}

SSE (LangGraph / CrewAI / Cursor)

http://127.0.0.1:8001/sse

Supported Models (routing catalogue)

Testing

uv run --group test pytest
# 118 tests, 100% coverage

Architecture

token-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