Skip to main content
Glama
alphaparkinc

genpark-agent-context-dynamic-compressor-skill

Official

Related Servers

Alternatives to genpark-agent-context-dynamic-compressor-skill

No user-submitted related servers found.

    Related Servers

    • F
      license
      Not graded
      quality
      B
      maintenance
      Enables MCP-compatible clients and autonomous agents to dynamically compress long-horizon context by pruning verbose tool outputs, HTML boilerplate, and semantic redundancy by 40-70%. It reduces token burn and latency through deterministic, zero-dependency processing.
      7
      -
    • A
      license
      Not graded
      quality
      C
      maintenance
      A task-aware context compression layer for Agent workflows, RAG pipelines, and AI Coding assistants, reducing noisy logs, retrieval chunks, and code context into high-signal LLM inputs via CLI, Python SDK, and MCP.
      352
      MIT
    • 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
      -
    • A
      license
      A
      quality
      A
      maintenance
      Provides reversible context compression for AI agents, reducing token usage while preserving the ability to retrieve original content, and serves as an MCP server for integration with tools like GitHub Copilot and Claude Code.
      3
      1
      Apache 2.0
    • A
      license
      A
      quality
      A
      maintenance
      tooltrim reduces the tokens agents spend re-reading bloated tool results. Run it as an MCP server exposing compress and expand_tool_output, or as a gateway in front of any upstream MCP server: it re-exposes the upstream tools unchanged and shrinks each result (HTML/JSON/logs/tables) before it reaches the model, keeping the relevant content only.
      2
      2
      MIT