Cycles MCP Server — AI agent runtime control over Model Context Protocol

MCP server that gives any MCP-compatible AI agent (Claude Code, Cursor, Windsurf, custom agents) runtime budget, action, and audit authority — enforce LLM cost limits, tool call caps, action permissions, and audit trails before execution, with zero agent code changes. Connect via MCP and use the budget tools (cycles_reserve, cycles_commit, cycles_release, cycles_decide) directly from the agent's tool-calling loop. Powered by Cycles.

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

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