hive-compute-mcp

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 cores

pip install hive-mcp

hive broker
# Prints the shared secret and starts listening

Worker 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 broker

This 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_b