Mibayy/token-savior
by Various
The MCP server that turns Claude into the only coding agent hitting 100% on a real benchmark. -77% active tokens, -76% wall time, 0 losses across 96 tasks on Claude Opus 4.7. Struc
MCP
Mibayy/token-savior
Added 1 June 2026
Overview
Mibayy/token-savior is a Model Context Protocol (MCP) server that optimizes Claude as a coding agent. It reduces active tokens by 77% and wall time by 76% while maintaining 100% task completion on a benchmark of 96 tasks for Claude Opus 4.7. The tool uses structural code navigation and persistent memory to achieve these savings and works with any MCP client.
Best for
Best for
Developers using Claude Opus for complex coding tasks who need to minimize token consumption and latency
Use cases
- Boosting Claude's coding efficiency and reducing token costs
- Persisting context across multiple coding sessions
- Navigating and understanding large codebases with minimal token usage
Notes
Mibayy/token-savior is a Model Context Protocol (MCP) server that optimizes Claude as a coding agent. It reduces active tokens by 77% and wall time by 76% while maintaining 100% task completion on a benchmark of 96 tasks for Claude Opus 4.7. The tool uses structural code navigation and persistent memory to achieve these savings and works with any MCP client.
929 stars on GitHub. Last updated 2026-05-26. Licensed MIT.
Use cases
- Boosting Claude’s coding efficiency and reducing token costs
- Persisting context across multiple coding sessions
- Navigating and understanding large codebases with minimal token usage
Pros
- Demonstrated 77% token and 76% time reduction with no task losses
- Works universally with any MCP client
- Open-source with active community support (929 stars)
Cons
- Benchmark may not cover all real-world coding scenarios
- Requires Claude Opus model and MCP client setup
- Optimizations may not generalize to other LLMs or tasks
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Demonstrated 77% token and 76% time reduction with no task losses
- Works universally with any MCP client
- Open-source with active community support (929 stars)
Cons
- Benchmark may not cover all real-world coding scenarios
- Requires Claude Opus model and MCP client setup
- Optimizations may not generalize to other LLMs or tasks
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