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Context Pipe MCP Server

@luismichio

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基本情報

カテゴリ

その他

トランスポート

stdio

作者

luismichio

投稿者

Luis Michio Kobayashi

Context Pipe MCP Server について

概要はまだありません

Context Pipe MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "context-pipe": {
      "command": "context-pipe-server",
      "args": [],
      "env": {
        "PIPE_CONFIG_PATH": "pipes.json"
      }
    }
  }
}

Context Pipe MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Context Pipe?

Context Pipe is a high-performance orchestration layer that chains AI tool calls through context streams, inspired by Unix terminal piping. It integrates MCP tools, shell commands, scripts, and binaries into named pipes that refine raw data into dense, high-signal content before it reaches an LLM. It is built for developers and AI engineers who need to reduce context window noise and make tool outputs composable and production-grade.

How to use Context Pipe?

Install the Python package (mcp-context-pipe) via pip, then define pipes in a pipes.json file (project-level) with optional global overrides in ~/.mcp-pipe.json. Use the mcp-pipe CLI to run named pipes (mcp-pipe run <pipe>), invoke MCP tools directly from the shell (mcp-pipe tool <server> <tool>), or inject IDE hooks via pipe_onboard. The Rust-native cpipe binary is also available as a standalone CLI or Tauri sidecar for sub‑2ms startup latency.

Key features of Context Pipe

  • Unix pipe model for AI: chain any stdin/stdout tool into a named pipe
  • MCP node type: call any MCP tool as a first-class pipe node
  • Dynamic pipes: agents construct ad‑hoc node lists at runtime
  • A2A agent handoff: distil Agent A’s output before Agent B sees it
  • Context Balance Sheet: per‑run accounting of input/output bytes and latency
  • Shadow MCP Registry: keep utility MCP servers invisible until needed

Use cases of Context Pipe

  • Crawl a webpage, filter for security keywords, convert to Markdown, and distill to a summary
  • Pass an error log through a semantic sifter, then pipe the output into a grep for critical issues
  • Hand off processed context between agents in CrewAI, LangGraph, or Google ADK without bloating the context window
  • Automatically refine tool outputs in an IDE (Cursor, VS Code, Windsurf, etc.) via subconscious interceptors
  • Audit and trace the entire context supply chain across multiple refineries in one pipe

よくある質問

1

What dependencies does Context Pipe require?

Python 3.10+ is required for the MCP server and Python orchestrator. The Rust native core (`cpipe`) is optional but recommended for sub‑2ms startup latency; it is available as a Tauri sidecar, standalone CLI, or Cargo library.

2

How is Context Pipe configured?

Pipe definitions live in `pipes.json` (project‑level) and optionally `~/.mcp-pipe.json` (global, merged with local precedence). No code changes are needed to add, modify, or re‑route pipes.

3

What transport does Context Pipe use?

Context Pipe nodes are real OS subprocesses communicating via stdin/stdout byte streams. The orchestration engine uses `shell=False` (no injection surface) and enforces per‑node timeouts. IDE hooks use the MCP protocol over stdio.

4

Does Context Pipe support authentication?

Authentication is not covered in the readme; pipes trust the local environment. MCP tools called via `mcp-pipe tool` inherit the servers’ own authentication (e.g., GitHub tokens) from their server registration in `pipes.json`.

5

What are the known limitations of Context Pipe?

For the OpenCode IDE, the `AGENTS.md` SOP mandate is the active strategy (no native hook yet). The orchestrator enforces non‑UTF8 robustness with `errors="replace"` and null‑safe reading. All nodes run with `shell=False` to eliminate injection risks.

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