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Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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 | — |
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 mcpVS 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
- 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