Circus MCP
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Circus MCPlist all managed processes"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Circus MCP
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 all managed processes |
|
| Process state and PID |
|
| Start a process |
|
| Stop a process |
|
| Restart a process |
|
| Add a new process dynamically |
Claude Code
claude mcp add circus-mcp -- uv run circus-mcp mcpVS Code / Cursor
.vscode/mcp.json:
{
"servers": {
"circus-mcp": {
"command": "uv",
"args": ["run", "circus-mcp", "mcp"]
}
}
}Circus MCP vs Supervisord MCP
Circus 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 |
| Throws error if already running |
Transport | ZeroMQ (async) | HTTP XML-RPC (sync) |
Best for | AI agent workflows | Existing Supervisord environments |
Documentation
AI Token Reduction Solution — Token cost analysis, team-scale projections, research references
AI & MCP Technical Background — Architecture, MCP hosting, tenant isolation
Installation & CLI Reference — Setup, configuration, full command reference
License
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Maintenance
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