Cycles MCP Server
Cycles MCP Server
MCP server for Cycles — runtime budget authority for autonomous agents.
Installation
npm install @runcycles/mcp-server
Why use this?
Autonomous AI agents (Claude, GPT, custom agents) call LLMs, invoke tools, and hit external APIs — but have no built-in way to cap how much they spend. A single agent loop can burn through hundreds of dollars before anyone notices. Multiply that across tenants and teams, and cost control becomes a real problem.
This MCP server gives any MCP-compatible agent a runtime budget authority: a set of tools to check, reserve, spend, and release budget before and after every costly operation. The agent asks "can I afford this?" before acting, and reports what it actually used afterward.
Who needs this:
- Platform teams building multi-tenant agent systems that need per-customer or per-workspace spend limits
- Agent developers who want agents to self-regulate — degrade to cheaper models when budget is low, skip optional tool calls, reduce retries
- Enterprises deploying AI agents that need guardrails so a runaway agent can't blow through a budget
Why MCP specifically:
MCP is the standard protocol that AI hosts (Claude Desktop, Claude Code, Cursor, Windsurf, custom agents) use to discover and call tools. By exposing Cycles as an MCP server, any MCP-compatible agent gets budget awareness as a plug-in — just add the server to your config. No SDK integration in the agent's own code required.
The server also ships built-in prompts so an AI assistant can help you design your budget strategy, generate integration code, and diagnose budget overruns — not just enforce budgets at runtime.
Use Cases
Coding agent with a per-task dollar cap
You run a Claude Code agent that writes and iterates on code. Each task should cost no more than $5. The agent calls cycles_reserve before every LLM call with a cost estimate in USD_MICROCENTS. If the reservation comes back DENY, the agent stops and reports "budget exhausted" instead of silently racking up charges. When the call completes, cycles_commit records the actual token cost so the running total stays accurate.
Multi-tenant SaaS with per-customer budgets
Your platform lets customers deploy AI assistants. Each customer has a monthly budget. The agent calls cycles_check_balance at the start of a conversation to see what's left, then cycles_reserve before each tool invocation (web search, code execution, API calls). If customer Acme is near their limit, the decision comes back ALLOW_WITH_CAPS — the agent automatically drops to a cheaper model and skips optional tools. Customer budgets are isolated; one customer's heavy usage never affects another.
Multi-agent pipeline with shared budget
You have an orchestrator that fans out to specialist agents — a researcher, a coder, and a reviewer. All three draw from the same workflow budget. Each agent calls cycles_reserve before its work; the Cycles server tracks concurrent reservations so the total never exceeds the workflow limit. If the researcher burns through 80% of the budget, the coder's next reservation gets DENY and the orchestrator can decide to skip the review step instead of going over budget.
Long-running data pipeline with heartbeats
An agent processes a large dataset in chunks, each chunk taking several minutes. It calls cycles_reserve with a 5-minute TTL before each chunk, then cycles_extend every 60 seconds to keep the reservation alive while processing. If the agent crashes, the reservation expires automatically and the locked budget returns to the pool — no manual cleanup needed.
Fire-and-forget usage metering
You have an existing system that already makes LLM calls and you just want to track spend, not gate it. After each call completes, the agent fires cycles_create_event with the actual cost. No reservation needed — the event is applied atomically to all budget scopes (tenant, workspace, app). You get a real-time spend dashboard without changing your existing call flow.
See Project repo for more details.
Server Config
{
"mcpServers": {
"cycles": {
"command": "npx",
"args": [
"-y",
"@runcycles/mcp-server"
],
"env": {
"CYCLES_BASE_URL": "http://localhost:7878",
"CYCLES_API_KEY": "<YOUR_API_KEY>"
}
}
}
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