MCP server gives your agent a budget

Implements a token budget system that tracks and enforces spending limits across agent interactions by intercepting LLM API calls through the MCP protocol. The system maintains a budget state machine that monitors cumulative token consumption (input + output tokens) and prevents operations that would exceed allocated limits, enabling cost-aware agent execution without modifying underlying LLM provider APIs.

Maintains real-time accounting of token usage across all LLM API calls within an agent session, parsing response metadata from providers to extract input/output token counts and aggregating them into a consumption ledger. Exposes consumption metrics via MCP resources or tool responses, enabling agents and developers to query current spending and remaining budget at any point during execution.

Implements conditional execution logic that gates agent operations based on remaining budget, preventing tool calls, LLM invocations, or workflow steps when insufficient tokens remain. The system can enforce hard stops (reject operations immediately) or soft limits (warn and allow with confirmation), and integrates with agent planning systems to enable budget-aware decision-making during task decomposition.

Aggregates token budgets across multiple LLM providers (OpenAI, Anthropic, etc.) into a single unified budget pool, tracking consumption from all providers against the same limit. The system routes agent requests to available providers based on budget availability and cost efficiency, enabling agents to dynamically select providers without exceeding the global budget.

Analyzes prompts and suggests optimizations to reduce token consumption when budget is constrained, such as removing verbose instructions, shortening examples, or using more concise phrasing. The system may automatically apply optimizations (e.g., truncating context, summarizing documents) when remaining budget falls below a threshold, trading prompt quality for cost efficiency.

Manages budget lifecycle with support for periodic resets (daily, hourly, per-session) and renewal policies, enabling time-based or event-based budget allocation. The system tracks budget windows, enforces per-window limits, and can implement rolling budgets or quota systems with configurable renewal intervals.

Enables agents to automatically fall back to cheaper models or model variants when budget is constrained, or to select the most cost-efficient model for a given task based on estimated cost and quality trade-offs. Implements a model selection layer that evaluates multiple model options (e.g., GPT-4 vs. GPT-3.5, Claude 3 Opus vs. Haiku), estimates costs for each, and routes requests to the cheapest option that meets quality requirements.

Filters or prioritizes available tools and functions based on their estimated token cost and relevance to the agent's task, preventing the agent from calling expensive tools when budget is constrained. Implements a tool registry that annotates each tool with cost metadata (e.g., 'this tool adds 500 tokens'), and dynamically filters the tool list presented to the agent based on budget state and cost-benefit analysis.