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rlawogh1005

green-mcp

by rlawogh1005

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Alternatives to green-mcp

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    • A
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      A
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      Enables token counting, usage tracking, and cost calculation for LLM APIs, with a proxy server mode to automatically intercept and monitor API requests from clients like Cursor and Windsurf.
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    • F
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      Estimates the environmental footprint of your AI use — energy (kWh), miles driven, water used for cooling, and CO₂ — plus a prompt-efficiency score, working with any AI client by measuring token usage.
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    • F
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      Local LLM cost & token forensics proxy with anomaly detection, enabling security teams to scan for cost anomalies and abuse patterns, and expose results via MCP for autonomous agents.
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    • A
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      quality
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      A local-first, multi-provider cost meter for LLM usage, exposed as MCP tools. Captures every call into a local SQLite ledger and lets any coding agent query spend, compare providers, and get recommendations — no cloud, no account. First-class support for Chinese providers (Qwen, DeepSeek) alongside Anthropic and OpenAI.
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    TDQS

    A4.3/5.0

    Scored across 6 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: measuring vs comparing energy vs tokens, verifying equivalence, and checking backend availability. No two tools overlap in function.

    Naming Consistency4/5

    Most tools follow a consistent verb_noun pattern (compare_energy, compare_tokens, measure_energy, measure_tokens, verify_equivalence). However, energy_backend_info breaks the pattern with a noun_noun structure, causing a minor inconsistency.

    Tool Count5/5

    With 6 tools, the server is well-scoped. It covers all core operations (measure, compare, verify, and check backend) without being overly numerous or too sparse.

    Completeness5/5

    The tool set is complete for its domain: it provides the necessary steps to measure energy/tokens, compare two commands, verify equivalence, and check measurement availability. No obvious gaps.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues