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DCx7C5

token-optimization-mcp

by DCx7C5
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Python
Remote

token-optimization-mcp v0.2.0

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

Tools

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

Related MCP server: claw-tsaver

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

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

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)

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

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
Install Server
F
license - not found
A
quality
D
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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