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An MCP server designed to optimize the working context of AI agents. It prevents context bloat and the "Lost in the Middle" phenomenon by distilling action histories into concise summaries while maintaining a clear core objective. AIエージェントのワーキングコンテキストを最適化するためのMCPサーバーです。行動履歴を簡潔な要約へと蒸留し、明確な大目標を維持することで、コンテキストの肥大化と「Lost in the Middle」現象を防ぎます。
mcp-working-context-optimizer is an MCP server entry in ChatHome Skills, sourced from GitHub MCP Search and scored 38/100. An MCP server designed to optimize the working context of AI agents. It prevents context bloat and the "Lost in the Middle" phenomenon by distilling action hist. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding mcp-working-context-optimizer to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
git clone https://github.com/globalpocket/mcp-working-context-optimizer.gitReview the upstream source before running this command.
Scan coverage: Install metadata and repository README.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
{
"fullName": "globalpocket/mcp-working-context-optimizer"
}An MCP server designed to optimize the working context of AI agents. It prevents context bloat and the "Lost in the Middle" phenomenon by distilling action histories into concise summaries while maintaining a clear core objective. AIエージェントのワーキングコンテキストを最適化するためのMCPサーバーです。行動履歴を簡潔な要約へと蒸留し、明確な大目標を維持することで、コンテキストの肥大化と「Lost in the Middle」現象を防ぎます。