{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-47780622","slug":"mcp-server-gives-your-agent-a-budget","name":"MCP server gives your agent a budget","type":"mcp","url":"https://l6e.ai","page_url":"https://unfragile.ai/mcp-server-gives-your-agent-a-budget","categories":["mcp-servers","app-builders"],"tags":["hackernews","show-hn"],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-47780622__cap_0","uri":"capability://tool.use.integration.token.budget.allocation.and.enforcement","name":"token-budget allocation and enforcement","description":"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.","intents":["I need to run my AI agent with a hard spending cap to control cloud costs","I want to prevent runaway token consumption from expensive multi-step agent workflows","I need to allocate different token budgets to different agents or users in a shared system","I want to track token spending across multiple LLM provider calls in a single agent session"],"best_for":["teams running cost-sensitive AI agents in production","developers prototyping multi-step agentic workflows with uncertain token costs","organizations with per-user or per-project token budgets","builders integrating multiple LLM providers and needing unified cost control"],"limitations":["Budget enforcement is post-hoc (tokens are counted after API calls complete, not predicted beforehand)","No built-in token estimation for prompts before execution — requires external tokenizer","Budget state is ephemeral unless explicitly persisted to external storage","Cannot retroactively refund tokens if a call exceeds remaining budget mid-execution"],"requires":["MCP client implementation (Claude SDK, or custom MCP client)","Active connection to at least one LLM provider (OpenAI, Anthropic, etc.)","Initial budget allocation parameter at agent initialization"],"input_types":["budget amount (integer, token count)","LLM API requests (prompts, messages, function calls)"],"output_types":["budget remaining (integer)","budget exceeded error (structured error response)","token consumption report (structured metadata)"],"categories":["tool-use-integration","cost-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47780622__cap_1","uri":"capability://data.processing.analysis.token.consumption.tracking.and.reporting","name":"token consumption tracking and reporting","description":"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.","intents":["I want to see a real-time breakdown of how many tokens my agent has consumed so far","I need to log token spending per agent step for billing or analytics purposes","I want to alert or pause an agent when token consumption reaches a threshold (e.g., 80% of budget)","I need to compare token efficiency across different agent strategies or prompts"],"best_for":["developers debugging token efficiency of agentic workflows","teams implementing chargeback or billing systems for shared AI infrastructure","researchers comparing prompt engineering strategies by token cost","operators monitoring agent health and cost trends in production"],"limitations":["Reporting granularity depends on LLM provider's token count metadata — some providers may not expose detailed breakdowns","No built-in historical persistence — consumption data is lost if agent session terminates without explicit export","Token counting accuracy varies by provider (OpenAI's tiktoken vs Anthropic's token counting may differ slightly)","Async agent calls may have race conditions in consumption aggregation if not properly synchronized"],"requires":["MCP server running with budget tracking enabled","LLM provider that returns token usage metadata in API responses (OpenAI, Anthropic, etc.)","Mechanism to query or subscribe to consumption updates (polling or event-based)"],"input_types":["LLM API responses with token metadata","query parameters (time range, agent ID, etc.)"],"output_types":["consumption summary (JSON: total_tokens, input_tokens, output_tokens, timestamp)","consumption timeline (array of per-call breakdowns)","budget utilization percentage (float 0-100)"],"categories":["data-processing-analysis","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47780622__cap_2","uri":"capability://planning.reasoning.budget.aware.agent.execution.control","name":"budget-aware agent execution control","description":"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.","intents":["I want my agent to gracefully degrade or stop when approaching budget limits instead of failing mid-task","I need to implement a two-tier system where critical agent operations proceed but exploratory steps are skipped when budget is low","I want the agent to choose cheaper LLM models or shorter prompts when budget is constrained","I need to prevent cascading failures where one expensive operation consumes the entire budget"],"best_for":["teams running long-running agents with unpredictable token costs","builders implementing cost-aware agentic systems with fallback strategies","organizations with strict per-request or per-session token budgets","developers building agents that adapt behavior based on resource constraints"],"limitations":["Requires agent code to be budget-aware or use a framework that supports budget-aware planning","Hard stops may leave tasks incomplete — no built-in rollback or cleanup mechanism","Budget predictions for future steps are not provided — agent must estimate costs independently","Soft limits require user interaction or external approval, adding latency to agent execution"],"requires":["MCP server with budget enforcement enabled","Agent framework that can handle budget-related errors or constraints (e.g., Claude with tool use)","Initial budget allocation and threshold configuration"],"input_types":["remaining budget (integer)","planned operation (tool call, LLM invocation, etc.)","threshold configuration (hard limit, soft limit percentage)"],"output_types":["execution decision (allow/deny/warn)","alternative operation suggestion (cheaper model, shorter prompt, etc.)","budget status update (remaining tokens, operations blocked)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47780622__cap_3","uri":"capability://tool.use.integration.multi.provider.token.budget.pooling","name":"multi-provider token budget pooling","description":"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.","intents":["I want to use multiple LLM providers but enforce a single global token budget across all of them","I need to automatically failover to a cheaper provider when the primary provider would exceed budget","I want to optimize cost by routing requests to the most token-efficient provider for each task","I need to prevent any single provider from consuming the entire budget in a multi-provider setup"],"best_for":["teams using multiple LLM providers for redundancy or cost optimization","builders implementing provider-agnostic agent systems","organizations with heterogeneous LLM provider contracts and budgets","developers optimizing for cost across providers with different pricing models"],"limitations":["Token count definitions vary across providers — pooling may be inaccurate if providers use different tokenization","No built-in cost normalization — pooling by tokens doesn't account for different per-token pricing","Provider failover adds latency and complexity to request routing","Budget allocation across providers must be manually configured or requires external optimization logic"],"requires":["MCP server with multi-provider support","API keys or credentials for multiple LLM providers","Provider configuration specifying token costs or efficiency metrics","Routing logic to select providers based on budget and cost"],"input_types":["provider list (array of provider names and credentials)","global budget (integer, token count)","cost metrics per provider (tokens per dollar, latency, etc.)"],"output_types":["provider selection (recommended provider for current request)","pooled consumption (total tokens across all providers)","per-provider breakdown (tokens consumed by each provider)"],"categories":["tool-use-integration","cost-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47780622__cap_4","uri":"capability://text.generation.language.budget.aware.prompt.optimization","name":"budget-aware prompt optimization","description":"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.","intents":["I want the agent to automatically shorten prompts when budget is running low","I need suggestions for how to reduce token consumption without losing task quality","I want to maintain a library of prompt variants optimized for different budget levels","I need to understand which parts of my prompts consume the most tokens"],"best_for":["developers optimizing long-running agents with variable budgets","teams managing cost-sensitive production agents","builders implementing adaptive prompting strategies","researchers studying token efficiency of different prompt structures"],"limitations":["Automatic prompt optimization may degrade task quality or accuracy","No built-in evaluation of optimization impact — requires external validation","Optimization suggestions are heuristic-based and may not be optimal for all tasks","Requires prompt structure analysis which may not work with all prompt formats (e.g., images, structured data)"],"requires":["MCP server with prompt analysis capability","Tokenizer for accurate token counting (tiktoken, Anthropic's tokenizer, etc.)","Optimization rules or templates (library of prompt variants)"],"input_types":["prompt text (string)","budget constraint (integer, remaining tokens)","optimization preference (aggressive, conservative, etc.)"],"output_types":["optimized prompt (string, shorter version)","optimization suggestions (array of recommendations with token savings)","token reduction estimate (integer, predicted tokens saved)"],"categories":["text-generation-language","cost-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47780622__cap_5","uri":"capability://automation.workflow.budget.reset.and.renewal.scheduling","name":"budget reset and renewal scheduling","description":"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.","intents":["I want to allocate a daily token budget that resets at midnight","I need per-user or per-session token budgets that reset independently","I want to implement a rolling 7-day budget window for cost tracking","I need to handle budget renewal when a user subscribes to a higher tier"],"best_for":["teams implementing multi-tenant AI systems with per-user budgets","SaaS platforms offering tiered AI agent access","organizations with daily or hourly cost limits","developers building subscription-based AI services"],"limitations":["Budget reset timing depends on system clock — distributed systems may have clock skew issues","No built-in persistence of budget history — requires external storage for audit trails","Renewal policies must be manually configured — no automatic tier-based renewal","Race conditions possible in concurrent budget resets without proper locking"],"requires":["MCP server with scheduling capability","Time source (system clock or external time service)","Budget configuration with renewal intervals and policies","Optional: persistent storage for budget history and audit logs"],"input_types":["budget amount (integer)","renewal interval (duration: daily, hourly, per-session, etc.)","renewal policy (reset, rollover, accumulate, etc.)","user or session identifier"],"output_types":["current budget window (start time, end time, remaining tokens)","next renewal time (timestamp)","budget history (array of past windows with consumption)"],"categories":["automation-workflow","cost-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47780622__cap_6","uri":"capability://planning.reasoning.budget.constrained.multi.model.fallback.and.selection","name":"budget-constrained multi-model fallback and selection","description":"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.","intents":["I want my agent to use cheaper models when budget is low, without degrading quality","I need to choose between multiple models based on cost-to-quality trade-offs","I want to automatically fall back to a cheaper model if the primary model would exceed budget"],"best_for":["agents with flexible quality requirements (e.g., summarization, classification)","cost-sensitive applications where model selection is a tuning parameter","teams managing multiple model subscriptions and wanting to optimize spend"],"limitations":["quality trade-offs are heuristic-based (e.g., model size as proxy for quality) and may not reflect actual performance on specific tasks","fallback logic is sequential (try primary, then fallback); no parallel evaluation or A/B testing","no built-in learning from past model selections; quality metrics must be provided externally"],"requires":["multiple model credentials (API keys for different providers/models)","cost estimates for each model (from provider pricing or cached data)","optional quality metrics or task-specific model rankings"],"input_types":["agent request with task type or quality requirements","list of candidate models with cost and quality metadata","budget constraints"],"output_types":["selected model identifier","cost estimate for selected model","fallback chain (if primary model unavailable)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47780622__cap_7","uri":"capability://tool.use.integration.budget.aware.function.calling.and.tool.use.filtering","name":"budget-aware function calling and tool use filtering","description":"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.","intents":["I want to prevent my agent from calling expensive tools when budget is low","I need to prioritize cheap tools over expensive ones for cost-sensitive tasks","I want to understand the token cost of each tool before my agent uses it"],"best_for":["agents with heterogeneous tool costs (e.g., web search vs. local database lookup)","cost-sensitive applications where tool selection is a tuning parameter","teams implementing cost governance policies for tool use"],"limitations":["tool cost estimates are static and don't account for dynamic factors (e.g., search result length, API response size)","filtering is binary (include/exclude); no soft constraints or cost-aware ranking of tools","requires manual annotation of tool costs; no automatic cost profiling"],"requires":["tool registry with cost metadata per tool","budget state (remaining tokens)","cost threshold configuration"],"input_types":["tool definitions with cost annotations","agent request context","budget constraints"],"output_types":["filtered tool list (available tools given budget)","tool cost estimates","tool selection recommendations"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":33,"verified":false,"data_access_risk":"moderate","permissions":["MCP client implementation (Claude SDK, or custom MCP client)","Active connection to at least one LLM provider (OpenAI, Anthropic, etc.)","Initial budget allocation parameter at agent initialization","MCP server running with budget tracking enabled","LLM provider that returns token usage metadata in API responses (OpenAI, Anthropic, etc.)","Mechanism to query or subscribe to consumption updates (polling or event-based)","MCP server with budget enforcement enabled","Agent framework that can handle budget-related errors or constraints (e.g., Claude with tool use)","Initial budget allocation and threshold configuration","MCP server with multi-provider support"],"failure_modes":["Budget enforcement is post-hoc (tokens are counted after API calls complete, not predicted beforehand)","No built-in token estimation for prompts before execution — requires external tokenizer","Budget state is ephemeral unless explicitly persisted to external storage","Cannot retroactively refund tokens if a call exceeds remaining budget mid-execution","Reporting granularity depends on LLM provider's token count metadata — some providers may not expose detailed breakdowns","No built-in historical persistence — consumption data is lost if agent session terminates without explicit export","Token counting accuracy varies by provider (OpenAI's tiktoken vs Anthropic's token counting may differ slightly)","Async agent calls may have race conditions in consumption aggregation if not properly synchronized","Requires agent code to be budget-aware or use a framework that supports budget-aware planning","Hard stops may leave tasks incomplete — no built-in rollback or cleanup mechanism","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.28,"quality":0.26,"ecosystem":0.31,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.326Z","last_scraped_at":"2026-05-04T08:10:01.171Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-server-gives-your-agent-a-budget","compare_url":"https://unfragile.ai/compare?artifact=mcp-server-gives-your-agent-a-budget"}},"signature":"S1ClPTqfGqi+hAOv60m38w1Df2bV4mMuM98WcbL4prBP45M/okoMvDHDSel1kIzrE38hLQnh/OV0hgosCVUCDg==","signedAt":"2026-06-21T15:55:54.496Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-server-gives-your-agent-a-budget","artifact":"https://unfragile.ai/mcp-server-gives-your-agent-a-budget","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-server-gives-your-agent-a-budget","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}