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Alternatives to agentfit-mcp

No user-submitted related servers found.

    Related Servers

    • F
      license
      Not graded
      quality
      B
      maintenance
      Enables autonomous agents to tokenize session context windows and compact dialogue history deterministically, exposing this capability to MCP-compatible clients like Claude Desktop and Cursor. Helps manage context length and reduce token usage in agent sessions through a zero-dependency Python MCP server.
      7
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    • F
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents and developers to dynamically tokenize session context windows and compact dialogue history into structured outputs via MCP, CLI, or Python client. Runs on pure standard library Python with no external dependencies for deterministic, low-latency execution.
      8
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    • A
      license
      Not graded
      quality
      A
      maintenance
      Provides MCP tools to profile and optimize LLM conversation context, identifying token waste and applying deterministic fixes to reduce context window usage.
      1,131 npm
      1
      MIT
    • A
      license
      B
      quality
      D
      maintenance
      Token usage tracker for OpenAI and Claude APIs with MCP (Model Context Protocol) support.
      6
      134 npm
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      MIT
    • A
      license
      A
      quality
      C
      maintenance
      Counts LLM prompt tokens and estimates API costs across OpenAI and Anthropic models directly inside MCP-compatible chat clients. Supports exact tokenization for OpenAI models and fallback approximation for Claude when no API key is present.
      4
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    TDQS

    A4.4/5.0

    Scored across 3 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: counting tokens, fitting messages under a token limit, and listing available estimator families. No overlap or confusion.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case (count_tokens, fit_messages, list_estimators), making them predictable and easy to understand.

    Tool Count5/5

    With 3 tools, the server is tightly scoped to token estimation and message fitting. Each tool earns its place, and the count is appropriate for this focused domain.

    Completeness4/5

    The tool surface covers core operations: counting tokens, fitting messages with multiple strategies, and listing estimators. A minor gap is the lack of a tool for direct model-specific tokenization, but the per-family estimator covers most use cases.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues