mcp-blackboard
About
An MCP server for managing context and memory for a multi-agent task execution based on agentic directed acyclic graph
Details
- Author
- peekwez
- Downloads
- 328
- Categories
- Other
Jump to
- Unified memory for agent context across workflows
- Filesystem abstraction via fsspec with optional drivers (S3, Azure Blob, GCS, etc.)
- Real‑time updates through Server‑Sent Events (SSE)
- Pluggable house‑keeping scheduler for pruning expired keys
- Container‑ready with deterministic builds and a slim Docker image (<90 MB)
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
mcp-blackboardCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
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.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"mcp-blackboard": {
"mcp-blackboard": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}McpServers
{
"mcp-blackboard": {
"command": "uv",
"args": [
"venv"
]
}
}mcp-blackboard
> Version 0.1.0 – A lightweight blackboard memory server for the Model Context Protocol (MCP)
mcp-blackboard exposes a simple HTTP/SSE interface that lets multiple AI agents store, and retrieve context and results—documents, embeddings, structured objects, and more—on a shared “blackboard”.
It is designed to be dropped into any MCP‑compatible workflow so your planner,
researcher, extractor, analyzer, writer, editor, and evaluator agents can collaborate without reinventing persistence.
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</div>
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Available Tools
MCP Tools
The following tools are available in mcp-blackboard:
- save_plan(plan_id: str, plan: dict | str) -> str
Save a plan to the shared state.
- mark_plan_as_completed(plan_id: str, step_id: str) -> str
Mark a plan step as completed in the shared state.
- save_result(plan_id: str, agent_name: str, step_id: str, description: str, result: str | dict) -> str
Save a result to the shared state.
- save_context_description(plan_id: str, file_path_or_url: str, description: str) -> str
Write a context description to the shared state.
- get_blackboard(plan_id: str) -> str | dict | None
Fetch a blackboard entry for a plan.
- get_plan(plan_id: str) -> str | dict | None
Fetch a plan from the shared state.
- get_result(plan_id: str, agent_name: str, step_id: str) -> str | dict | None
Fetch a result from the shared state.
- get_context(file_path_or_url: str, use_cache: bool = True) -> str
Read and convert media content to Markdown format.
File Cache Management Scheduler
- remove_stale_files(max_age: int = 3600) -> None
Remove files older than the specified age from the cache directory.
✨ Highlights
| Capability | Why it matters |
| --------------------------- | ----------------------------------------------------------------------------------------------- |
| Unified memory | One source of truth for agent context—no need for ad‑hoc scratch files or transient Redis keys. |
| Filesystem abstraction | Built on fsspec with optional drivers for S3, Azure Blob, GCS, ABFS, SFTP, SMB, and more. |
| Real‑time updates | Server‑Sent Events (SSE) stream context changes to connected agents instantly. |
| House‑keeping scheduler | Pluggable cron jobs automatically prune expired keys and refresh embeddings. |
| Container‑ready | Deterministic builds via uv lockfile; the slim Docker image is <90 MB. |
---
🚀 Quick Start
1. Local dev environment
```bash
git clone https://github.com/your‑org/mcp-blackboard.git
cd mcp-blackboard
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