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Circus MCP

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Circus MCP

PyPI version Python 3.10+ License: MIT

Cut 75-80% of AI agent debugging tokens in development cycle. Process management via MCP — structured tools replace shell commands.

75-80% Token Reduction for AI Agent Debugging in Development Cycle

AI agents debugging processes via raw shell commands (supervisorctl, ps, journalctl) burn most of their tokens on unstructured output parsing, repeated commands, and inter-step reasoning. Circus MCP replaces this with structured, bounded MCP tool responses.

Raw Commands

Circus MCP

Reduction

Tool calls per investigation

8-12

3-5

60-70%

Tokens per investigation

2,900-9,400

935-1,535

~75%

With retries (typical)

~10,000+

~2,000

~80%

Retry cost scaling

Exponential

Linear

—

Related MCP server: token-pilot

Process Management via MCP

Circus MCP exposes process lifecycle operations as MCP tools. AI agents call structured tools instead of parsing shell output.

Tool

Parameters

Description

list_processes

—

List all managed processes

get_process_status

name

Process state and PID

start_process

name

Start a process

stop_process

name

Stop a process

restart_process

name

Restart a process

add_process

name, command, numprocesses?, working_dir?

Add a new process dynamically

Claude Code

claude mcp add circus-mcp -- uv run circus-mcp mcp

VS Code / Cursor

.vscode/mcp.json:

{
  "servers": {
    "circus-mcp": {
      "command": "uv",
      "args": ["run", "circus-mcp", "mcp"]
    }
  }
}

Circus MCP vs Supervisord MCP

Circus MCP

Supervisord MCP

Dynamic process addition

Via API

Not supported (requires config file edit + reload)

Log retrieval

stdout + stderr in one call

Separate calls

System stats (CPU/memory)

Available

Not available

Idempotent operations

ensure_started / ensure_stopped

Throws error if already running

Transport

ZeroMQ (async)

HTTP XML-RPC (sync)

Best for

AI agent workflows

Existing Supervisord environments

Documentation

License

MIT — see LICENSE. Built on Circus.

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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