Hive

Distributed compute MCP server — pool idle LAN machines into a compute cluster for AI agents.

The Problem

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.

The Solution

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

Quick Start

1. Install

pip install hive-mcp

2. Start the Broker (host machine)

hive broker
# Prints the shared secret and starts listening

3. Join Workers (worker machines)

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!

4. Configure Claude Code

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_backtest(params_8)", priority=1)
  ...

MCP Tools

Tool	Description
create_batch	Create a named batch for grouping related tasks
close_batch	Close a batch after all tasks are submitted
get_batch_status	Get task counts and status for a batch
get_batch_results	Retrieve all results from a batch in one call
submit_task	Submit a Python or shell task (optionally to a batch)
get_task_status	Check if a task is queued, running, or complete
get_task_result	Retrieve the output of a completed task
pull_task	Pull a queued task back for local execution
report_local_result	Report result of a locally-executed pulled task
cancel_task	Cancel a pending or running task
list_workers	See all connected workers and their capacity
get_cluster_status	Overview of the entire cluster

Features

• Zero-config discovery — workers find the broker automatically via mDNS
• Adaptive capacity — workers monitor CPU and reject tasks when overloaded (--max-cpu 80)
• File transfer — send input files to workers, collect output files back
• Local fallback — pull queued tasks back when local CPU frees up
• Subprocess isolation — tasks can't crash the worker daemon
• Priority queue — higher-priority tasks run first
• Auto-reconnect — workers reconnect with exponential backoff
• Claude Code hook — hive context injects cluster info into every prompt
• Python SDK — programmatic access via HiveClient
• Shell tasks — run shell commands, not just Python

CLI Reference

hive broker                     # Start broker + MCP server
hive join                       # Join as worker (auto-discover broker)
hive join --broker-addr IP:PORT # Join with explicit address
hive join --max-cpu 60          # Limit CPU usage to 60%
hive join --max-tasks 4         # Hard cap at 4 concurrent tasks
hive status                     # Show cluster status
hive secret                     # Show/generate shared secret
hive context                    # Output machine + cluster info (for hooks)
hive tls-setup                  # Generate self-signed TLS certificates

Claude Code Hook

Add automatic cluster awareness to every prompt:

{
  "hooks": {
    "UserPromptSubmit": [
      {
        "command": "hive context",
        "timeout": 3000
      }
    ]
  }
}

This injects:

[hive-mcp] Local machine: 8 cores / 16 threads, CPU: 45%, RAM: 14GB free / 32GB total
[hive-mcp] Cluster: 2 workers online (20 cores), 0 queued, 3 active
[hive-mcp] Tip: 20 remote cores available via hive. Use submit_task() for overflow.

Python SDK

from hive_mcp.client.sdk import HiveClient

async with HiveClient("192.168.1.100", 7933, secret="...") as client:
    task = await client.submit("print('hello from hive')")
    result = await client.wait(task["task_id"])
    print(result["stdout"])  # "hello from hive"

How It Works

1. Broker runs on the host machine alongside Claude Code. It's both an MCP server (stdio, for Claude Code) and a WebSocket server (for workers).
2. Workers run on worker machines. They discover the broker via mDNS, authenticate with a shared secret, and wait for tasks.
3. Tasks are Python code strings or shell commands. The broker serializes them with cloudpickle and dispatches to workers.
4. Workers execute tasks in isolated subprocesses — a hung or crashing task can't affect the worker daemon.
5. Results flow back through WebSocket, including stdout, stderr, return values, and output files.

Security

• Shared secret — broker generates a 32-byte random token; workers must present it to connect
• TLS (optional) — run hive tls-setup to generate self-signed certificates
• Subprocess isolation — tasks run in separate processes, not in the worker daemon

Troubleshooting

Windows: MCP tools not loading

There is a known Claude Code bug where Windows drive letter casing (c:/ vs C:/) creates duplicate project entries in ~/.claude.json. The MCP config ends up under one casing while Claude Code looks up the other.

Fix: Open ~/.claude.json, search for your project path in the "projects" object, and ensure both case variants have identical mcpServers config. Or re-run claude mcp add from the same terminal type you use for Claude Code sessions.

Broker not starting

Check ~/.hive/broker.log for startup errors. Common causes:

• Port 7933 already in use (another broker instance)
• Python version mismatch between hive CLI and expected environment

Requirements

• Python 3.10+
• All machines on the same LAN (for mDNS discovery)
• Same Python version on broker and workers (for cloudpickle compatibility)

License

MIT