@peekwez
基本信息
An MCP server for managing context and memory for a multi-agent task execution based on agentic directed acyclic graph
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"mcp-blackboard": {
"command": "uv",
"args": [
"venv"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
mcp-blackboard is a lightweight blackboard memory server for the Model Context Protocol (MCP). It exposes an HTTP/SSE interface that lets multiple AI agents store, retrieve, and share context and results—documents, embeddings, structured objects—on a shared “blackboard.” It is designed for MCP‑compatible workflows where planner, researcher, extractor, analyzer, writer, editor, and evaluator agents collaborate without reinventing persistence.
Clone the repository, create an isolated environment with uv venv && uv sync, copy the sample env (samples/env-sample.txt) to .env, fill in credentials, then run uv run src/main.py. Alternatively, use docker compose up -d to start the FastAPI+SSE service along with a Redis instance. The API listens on http://127.0.0.1:8000 by default.
It provides a lightweight, unified memory layer specifically designed for MCP‑based multi‑agent workflows, with built‑in SSE real‑time updates and a pluggable filesystem backend (S3, Azure Blob, GCS, SFTP, SMB, etc.) without requiring ad‑hoc scratch files or transient Redis keys.
Python 3.12+ is required. The server uses FastAPI, APScheduler, and fsspec. A Redis instance is started via Docker Compose for in‑memory key, score, and embedding storage.
Data is stored in Redis by default. The filesystem abstraction (`fsspec`) also allows persisting context and results on remote storage such as S3, Azure Blob, GCS, ABFS, SFTP, or SMB, configured via environment variables.
The server uses HTTP with SSE for event transport (configurable via `MCP_TRANSPORT` as `sse` or `poll`). Authentication is not explicitly documented in the README; credentials for remote filesystems are provided via environment variables (e.g., `AWS_ACCESS_KEY_ID`, `AZURE_STORAGE_ACCOUNT`).
The README does not list explicit limits. The server relies on Redis for in‑memory storage and may be constrained by available memory and Redis configuration. Stale files can be removed via the `remove_stale_files` tool (default max age 3600 seconds).
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