Context Engine MCP Server

Compress logs, retrieval chunks, and code context into structured LLM-ready signal.

The short version

面向 Agent 工作流、RAG 管线、AI Coding 助手的任务感知 Context Compression Layer。

The tools it exposes

The server publishes 9 tools. What each one is for:

• embedding — aware reranking
• PyPI — https://pypi.org/project/melonelish-context-engine/
• HIGH — 与问题重叠度高，可能直接支持回答
• SUPPORT — 有帮助但不是核心证据
• CLI — 本地处理日志、样例和脚本任务
• compress_context — mode、budget、content 或 payload
• logs — 原始日志 / 简单截断
• rag — 直接 dump 检索 chunk
• code — 普通文件摘要

Getting it running

Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client.

How it compares

Things to watch

• It runs with your machine's permissions. That is convenient and also the reason to think about what you point it at before you approve a tool call.
• Keep per-call confirmation enabled while you learn its behaviour; it is the cheapest safeguard you have.

Recorded tools

• embedding — aware reranking
• compress_context — mode、budget、content 或 payload
• logs — 原始日志 / 简单截断
• rag — 直接 dump 检索 chunk
• code — 普通文件摘要

Setup and compatibility

This listing has no verified copy-paste launch configuration. Use the project documentation to confirm the entry point, runtime and authentication before registering it in your MCP client. A package name or repository alone is not a runnable configuration.

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