saikodi/hive-compute-mcp
Distributed compute MCP server — pool idle LAN machines into a compute cluster for AI agents
Distributed compute MCP server — pool idle LAN machines into a compute cluster for AI agents.
Running CPU-intensive agentic workloads (backtesting, simulations, hyperparameter sweeps) can peg your host machine at 100% with just 6-7 subagents. Meanwhile, other machines on your LAN sit idle with dozens of cores unused.
hive-mcp turns idle machines on your LAN into a unified compute pool, accessible via MCP from Claude Code, Cursor, Copilot, or any MCP-compatible AI tool.
Host Worker A Worker B
+-----------------+ +----------------+ +----------------+
| Claude Code | | hive worker | | hive worker |
| hive-mcp broker |<---->| daemon | | daemon |
| (MCP + WS) | ws | auto-discovered| | auto-discovered|
+-----------------+ +----------------+ +----------------+
8 cores 14 cores 6 cores
= 28 total corespip install hive-mcphive broker
# Prints the shared secret and starts listeningWorker machines are headless compute — they only need Python and hive-mcp. No Claude Code, no AI tools, no API keys. They just execute tasks and return results.
# Copy the secret from the broker, then:
hive join --secret <token>
# Auto-discovers broker via mDNS — no address needed!Register hive-mcp as an MCP server:
claude mcp add hive-mcp -- hive brokerThis writes the config to ~/.claude.json scoped to your current project directory.
Now Claude Code can submit compute tasks to your cluster:
You: "Run backtests for these 20 parameter combinations"
Claude Code: I'll run 6 locally and submit 14 to hive...
submit_task(code="run_backtest(params_7)", priority=1)
submit_task(code="run_bLoading reviews...