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Related Servers

Alternatives to nikhilnt

No user-submitted related servers found.

    Related Servers

    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables local-first tracking of AI token usage and costs from Claude Code and OpenCode, answering queries about tokens, models, sessions, and cost through MCP tools and a CLI, with no network calls.
      436 npm
      1
      MIT
    • A
      license
      A
      quality
      A
      maintenance
      Local-first dashboard + MCP server that parses Claude Code and Codex JSONL files into a SQLite cost / token tracker. Per-MCP and per-tool breakdown, session drill-down, dedup by request_id; never talks to vendor APIs
      5
      100 npm
      1
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      MCP server for estimating LLM token consumption and costs from project documents, code, and materials. Provides 8 tools including project budget planning, token/cost estimation, GitHub repo analysis, and pricing refresh.
      MIT
    • F
      license
      A
      quality
      C
      maintenance
      A local MCP server that tracks token usage and costs for Claude Desktop and Claude Code, providing a live dashboard at localhost:6789.
      5
      5 npm
      -
    • A
      license
      A
      quality
      A
      maintenance
      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.
      7
      4
      MIT
    • F
      license
      Not graded
      quality
      D
      maintenance
      Enables AI cost calculation, comparison, and optimization across major providers like Anthropic, OpenAI, Google, Meta, and Mistral. Supports cost estimation, budget-aware model finding, and token estimation through a simple API and MCP integration.
      -

    TDQS

    B3.4/5.0

    Scored across 8 tools

    Disambiguation5/5

    All eight tools have clearly distinct purposes: logging, summarizing, hinting, listing models/sessions, setting budgets, and exporting. No two tools overlap in functionality, ensuring unambiguous selection.

    Naming Consistency5/5

    Tool names follow a consistent verb_noun pattern with lowercase underscores (e.g., list_models, set_budget, export_csv). No mixing of conventions or verb styles.

    Tool Count5/5

    With eight tools, the server covers a focused domain (AI usage tracking and optimization) without being too sparse or overwhelming. Each tool serves a distinct and necessary function.

    Completeness4/5

    The tool surface covers core workflows: logging, viewing summaries, getting optimization hints, listing models/sessions, setting budgets, and exporting. Minor gap: no tool to delete or reset data, but the domain is primarily read-heavy and logging.

    Maintenance

    ActivityInactive
    ResponsivenessUnresponsive