tokens-saver-mcp
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- AlicenseBqualityCmaintenanceDelegates heavy, repetitive, and verifiable tasks like PDF extraction, code analysis, and log processing to a local LLM to reduce token consumption for frontier AI models, while keeping decision-making with the main AI.8MIT
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- AlicenseNot gradedqualityBmaintenanceEnables Claude Code and other MCP clients to offload bounded tasks such as code review, test stubs, commit messages, and format conversion to local or cheaper cloud LLMs, cutting token usage while the primary model remains the orchestrator.MIT
- AlicenseAqualityDmaintenanceOptimizes token costs by intelligently delegating low-complexity tasks to local LLMs via LiteLLM, enabling cost-effective development workflows.31MIT
TDQS
Scored across 7 tools
Each tool has a distinct, non-overlapping purpose: classification, code patch generation, diff summarization, JSON extraction, rewriting, text summarization, and task extraction. No ambiguity between tools.
All tools use the 'tsm_' prefix and snake_case. Most follow a verb_noun pattern (classify, summarize, rewrite), though 'tsm_codegen_small_patch' is slightly less consistent with its compound noun. Overall, naming is clear and predictable.
With 7 tools, the server is well-scoped for a utility focused on token-saving and text processing. Each tool addresses a common need without being excessive or insufficient.
The tool set covers major text operations: classification, extraction, transformation, and summarization. Minor gaps exist (e.g., no data masking or bulk processing), but the core functionality for offloading to budget models is complete.