TokenOracle

AI 与智能体

by victoryintech

Hosted MCP server for LLM cost estimation, model comparison, and budget-aware routing.

什么是 TokenOracle

Hosted MCP server for LLM cost estimation, model comparison, and budget-aware routing.

README

THIS REPO IS ARCHIVED AND THE SERVICE HAS BEEN SHUTDOWN

TokenOracle MCP

Token Oracle is a Model Context Protocol (MCP) server that estimates, compares, and controls LLM API costs before agents spend tokens. It exposes nine tools, four read-only Resources, and a cost_analysis_workflow Prompt template. It uses a proprietary pricing algorithm without a backing LLM to ensure deterministic budget workflows.

Designed to work with agent swarms backing one or zero employee companies, Token Oracle acts as a tiny CFO within your OpenClaw swarm keeping spend down and making suggestions to improve promptings.

Save them tokens, call Token Oracle today!

MCP tools exposed:

  • estimate_cost — Estimates the USD cost of a single LLM API call before execution. Input: task_description, prompt_text, task_type, or explicit token_count. Output: cost_usd, recommended_model, confidence, will_fit_context, pricing_updated. Annotations: readOnlyHint:true, idempotentHint:true, openWorldHint:false.
  • estimate_cost_batch — Prices up to 100 LLM tasks in a single call. Returns per-task breakdown, total_cost_usd, and cheapest_model_for_all. Use before starting any multi-step pipeline.
  • compare_models — Ranks LLM pricing across all supported providers for a given task. Returns models sorted by cost with speed_tier and quality_tier. Supports filtering by min_quality, max_cost_usd, and provider. Input: task_type, token_count, or prompt_text.
  • budget_check — Checks whether a planned task fits within a monthly budget. Returns can_proceed (boolean), remaining_budget_usd, budget_consumed_pct, and cheaper_alternatives with savings_pct. Input: monthly_budget_usd, current_spend_usd, and task description.
  • find_cheapest_for_budget — Inverse of budget_check. Given a budget_usd cap and task, returns the best model/quality combination within budget plus all alternatives ranked by quality then cost.

MCP Resources exposed:

  • token-oracle://meta — Machine-readable server capability document (version, model_count, pricing metadata)
  • token-oracle://models — Model IDs with metadata for discovery and validation
  • token-oracle://heuristics — Task-type token heuristics and keyword classifier patterns (auditable)
  • token-oracle://pricing/changelog — Append-only log of pricing changes; use to detect pricing updates over time

MCP Prompt exposed:

  • cost_analysis_workflow — Guided three-step prompt template: estimate_cost → compare_models → budget_check. Arguments: task_description, monthly_budget_usd (optional), quality_threshold (optional).

Supported providers: Anthropic, OpenAI, Google, Groq, Together AI, Mistral, DeepSeek, Cohere (100+ models).

Canonical agent workflow example: Goal: Run 500 product description generation tasks. Budget $50/mo, current spend $43.

  1. estimate_cost_batch({ tasks: [{ task_type: "generate", token_count: { input: 200, output: 800 }, quantity: 500 }] }) → total_cost_usd: $0.60
  2. compare_models({ token_count: { input: 200, output: 800 }, task_type: "generate", min_quality: "med", max_cost_usd: 0.002 }) → deepseek-v3 at $0.00035/task ($0.175 total) — 71% cheaper, same quality tier
  3. budget_check({ monthly_budget_usd: 50, current_spend_usd: 43, token_count: { input: 200, output: 800 }, model: "deepseek-v3" }) → can_proceed: true, remaining: $6.825, budget_consumed_pct: 2.4% Decision: Use deepseek-v3. Save $0.425 vs gpt-4o-mini.

Pricing:

  • Free: 1000 API calls/day, all hosted tools via the remote endpoint, no credit card
  • Pro ($29/mo): Unlimited calls, track_spend tool (persisted cost ledger per API key), priority throughput, spend attribution per agent/task/session

Contact: info@guffeyholdings.com

Hosted endpoint

  • Canonical remote MCP URL: https://mcp.guffeyholdings.com/TokenOracle
  • Canonical MCP name: com.guffeyholdings/token-oracle

Direct remote configuration:

json
{
  "mcpServers": {
    "token-oracle": {
      "type": "streamable-http",
      "url": "https://mcp.guffeyholdings.com/TokenOracle",
      "headers": {
        "X-API-Key": "${TOKEN_ORACLE_API_KEY}"
      }
    }
  }
}

npm bridge package

For local clients that still expect an npm-installed stdio server, use token-oracle-mcp.

Zero-input trial flow:

Start the bridge with no API key and, when the hosted service has trial auth enabled, it will automatically fetch and store a metered trial credential on first launch.

json
{
  "mcpServers": {
    "token-oracle": {
      "command": "npx",
      "args": ["-y", "token-oracle-mcp"]
    }
  }
}

One-time explicit login flow:

bash
npx -y token-oracle-mcp login

With --api-key, that validates and stores a paid hosted API key. Without --api-key, it requests and stores a hosted trial credential instead. After either flow, the MCP config does not need to inject TOKEN_ORACLE_API_KEY.

json
{
  "mcpServers": {
    "token-oracle": {
      "command": "npx",
      "args": ["-y", "token-oracle-mcp"]
    }
  }
}

If you prefer stateless setup, keep passing TOKEN_ORACLE_API_KEY as an environment variable instead.

Hosted trial behavior:

  • Trial credentials are metered and capped server-side
  • Once the hosted trial request limit is reached, the service returns an upgrade-required response
  • The hosted service reuses the same still-valid trial credential for the same claimant instead of minting a fresh token each time
  • Trial issuance is separately throttled and can be blocked by server-side abuse risk scoring
  • Later, a hosted upgrade flow can replace the stored trial credential with a paid credential without changing MCP config

Optional bridge environment variables:

  • TOKEN_ORACLE_API_KEY: optional hosted API key; overrides any stored credential
  • TOKEN_ORACLE_BASE_URL: override for the remote endpoint; defaults to https://mcp.guffeyholdings.com/TokenOracle
  • TOKEN_ORACLE_SUBJECT: optional end-user subject forwarded as X-Token-Oracle-Subject

Additional bridge commands:

  • npx -y token-oracle-mcp login: accept --api-key for paid auth, or fetch a hosted trial credential when no key is supplied
  • npx -y token-oracle-mcp logout: remove locally stored credentials

Capabilities

Tools:

  • estimate_cost
  • estimate_cost_batch
  • compare_models
  • budget_check
  • find_cheapest_for_budget
  • get_budget_status
  • list_request_activity
  • get_usage_summary
  • get_usage_leaderboard

Resources:

  • token-oracle://meta
  • token-oracle://models
  • token-oracle://heuristics
  • token-oracle://pricing/changelog

Prompts:

  • cost_analysis_workflow

Versioning

  • Hosted service version: 1.0.6
  • Bridge package version: 1.0.6

常见问题

TokenOracle 是什么?

Hosted MCP server for LLM cost estimation, model comparison, and budget-aware routing.

相关 Skills

Claude接口

by anthropics

Universal
热门

面向接入 Claude API、Anthropic SDK 或 Agent SDK 的开发场景,自动识别项目语言并给出对应示例与默认配置,快速搭建 LLM 应用。

想把Claude能力接进应用或智能体,用claude-api上手快、兼容Anthropic与Agent SDK,集成路径清晰又省心

AI 与智能体
未扫描165.9k

RAG架构师

by alirezarezvani

Universal
热门

聚焦生产级RAG系统设计与优化,覆盖文档切块、检索链路、索引构建、召回评估等关键环节,适合搭建可扩展、高准确率的知识库问答与检索增强应用。

面向RAG落地,把知识库、向量检索和生成链路系统串联起来,做架构设计时更清晰,也更少踩坑。

AI 与智能体
未扫描23.7k

多智能体架构

by alirezarezvani

Universal
热门

聚焦多智能体系统架构设计,梳理 Supervisor、Swarm、分层和 Pipeline 等模式,覆盖角色定义、通信协作与性能评估,适合规划稳健可扩展的 AI agent 编排方案。

帮你系统解决多智能体应用的架构设计与协同编排难题,适合构建复杂 AI 工作流,成熟度高、社区认可也很亮眼。

AI 与智能体
未扫描23.7k

相关 MCP Server

顺序思维

编辑精选

by Anthropic

热门

Sequential Thinking 是让 AI 通过动态思维链解决复杂问题的参考服务器。

这个服务器展示了如何让 Claude 像人类一样逐步推理,适合开发者学习 MCP 的思维链实现。但注意它只是个参考示例,别指望直接用在生产环境里。

AI 与智能体
89.1k

知识图谱记忆

编辑精选

by Anthropic

热门

Memory 是一个基于本地知识图谱的持久化记忆系统,让 AI 记住长期上下文。

帮 AI 和智能体补上“记不住”的短板,用本地知识图谱沉淀长期上下文,连续对话更聪明,数据也更可控。

AI 与智能体
89.1k

by deusdata

热门

持久化的代码库知识图谱,可跨会话保留上下文,在 session 重启或上下文压缩后仍能继续使用。

专治 AI 编程助手“会话失忆”,把代码库沉淀为持久知识图谱,重启或压缩上下文后也能无缝续上开发状态。

AI 与智能体
36.7k

评论