原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

Inferrail

作者:domondi1版本 0.4.18i★ –i在你的电脑上运行
注册名称io.github.domondi1/inferrail

安装

概览

作者提供的说明(英文原文)

Local, payload-free AI cost visibility for agents. Query spend by model, route, or tagged work.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 其他

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 需要你自己填写: INFERRAIL_RECEIPTS_PATH

你需要

  • PyPI已安装 Python 的 uv(用于 uvx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "inferrail": {
      "command": "uvx",
      "args": [
        "inferrail",
        "mcp"
      ],
      "env": {
        "INFERRAIL_RECEIPTS_PATH": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add inferrail --env INFERRAIL_RECEIPTS_PATH=your-value -- uvx inferrail mcp

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "inferrail": {
      "command": "uvx",
      "args": [
        "inferrail",
        "mcp"
      ],
      "env": {
        "INFERRAIL_RECEIPTS_PATH": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "inferrail": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "inferrail",
        "mcp"
      ],
      "env": {
        "INFERRAIL_RECEIPTS_PATH": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

  • INFERRAIL_RECEIPTS_PATH env
    Absolute path to your Inferrail receipts file (JSONL or SQLite). Used when a tool call gives no receipts_path. Default: ./inferrail-receipts.jsonl

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 domondi1 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“其他”中的其他服务器比较i

  • 星标数多于此分类中其他 18584 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 18584 个服务器的 86% 更新
  • 基础检查结果等于或优于此分类中其他 18584 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Inferrail 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.4.18)于6天前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它?

同一分类中有:Spec Driven Development, Remote Desktop Commander, aamio rendezvous

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:↓ 每周下载量i★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Spec Driven Development

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

作者:formulahendry起步阶段↓ 66/周★ 44014个月前更新

在你的电脑上运行

Remote Desktop Commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

作者:desktop-commander正在增长★ 2113天前更新

在云端运行✓ desktopcommander.app

aamio rendezvous

Meet an agent you have not met, exchange messages that expire, and prove it happened. No account.

作者:aamio.at起步阶段今天更新

在云端运行✓ aamio.at

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

Inferrail

Spending limits, receipts and paid execution for AI agents.

Give each AI job a dollar budget that's enforced before the provider is called, and a cost receipt that never stores what the model said.

Quickstart · For agents · How it works · Docs · tryinferrail.com

A real run of the gateway and dashboard with a local stand-in model: one job with a $0.04 limit, four calls answered, two refused with HTTP 402 before reaching the model. How it was recorded

What works today

Developer preview: interfaces may change before 1.0.

CapabilityWhat it doesWhereStatus
GatewayOpenAI- and Anthropic-compatible proxy you run yourself. A dollar budget per job, shared by parallel calls; a call that would exceed it gets HTTP 402 before the provider.pip install inferrailAvailable
Cost receiptsOne record per call: tokens, price, cost, status, your tags. Never prompts or responses. Totals by job, customer or any tag.local SQLite or JSONLAvailable
MCP serverRead-only get_spend and get_health tools over your local receipts.inferrail mcpPreview
Inferrail HostedThe same per-run budgets run for you, shared across agents and machines. 250 runs a month free, then USDC credits over x402. Bring your own provider key.hosted guidePreview
Web ReadOne public web page as clean markdown with a receipt, paid per call by the agent over x402 or MPP.belowPreview
Agent purchases under a job budgetOne budget covering model calls and x402 purchases; a purchase that doesn't fit is refused before it's signed. Local proof, no mainnet.exampleExperimental

Also experimental: AP invoice-exception recovery, a hosted trial gateway, and Work Economics / Economic Authority (example; Base Sepolia testnet only).

Quickstart

Requires Python 3.11+.

Try it offline. No API key, no network calls, no provider charges:

pip install inferrail

inferrail demo inferrail report --by customer --receipts ./inferrail-demo-receipts.jsonl

The demo sends scripted requests through the real engine to a fake provider with made-up prices labelled DEMO, and prints what they cost by customer.

Give one run a budget, inside your Python app. No config file, no second process:

import inferrail

from openai import OpenAI

base_url = inferrail.start() # the gateway, on a background thread in this process client = OpenAI(base_url=base_url, api_key="unused") # your key stays in OPENAI_API_KEY client.chat.completions.create( model="gpt-4o-mini", # any model your account can use; inferrail models lists priced ones max_tokens=200, messages=[{"role": "user", "content": "Summarize this contract."}], extra_headers={ "X-Inferrail-Attribute-Work-Id": "contract-review-42", # the run "X-Inferrail-Budget-Usd": "0.50", # its dollar ceiling }, )

Every call with the same work id shares that budget, including parallel calls; one that would push the run past it gets HTTP 402 before it reaches OpenAI. inferrail work contract-review-42 shows what the run cost.

More: run it as a shared gateway with the local dashboard, framework snippets (LangChain, LangGraph, OpenAI Agents SDK, CrewAI, Haystack, LlamaIndex, Microsoft Agent Framework), and other clients, including the Anthropic SDK.

For agents

Start at [tryinferrail.com/llms.txt](https://tryinferrail.com/llms.txt): what's live, with links to each service's guide and the OpenAPI contract.

Web Read sells one public web page as clean markdown, with its word count, a SHA-256 of the content and a receipt. $0.002 per delivered page, paid over x402 (USDC on Base) or MPP (Tempo); no account or API key. The payment is checked first and settled only after the page is delivered, so refusals (robots.txt, non-public addresses, non-text, over 2 MB, target-site errors) are never charged. Retrying the same url and request_id from the same wallet within 10 minutes returns the page already paid for.

curl -i -X POST https://inferrail-cost-gateway.onrender.com/read/v1/page \

-H 'content-type: application/json' -d '{"url": "https://example.com/"}'

HTTP 402 with the price; an x402 or MPP client pays and repeats the request

Guides: Web Read · Hosted · service source.

MCP. Ask your agent what a job cost, from your local receipts:

claude mcp add inferrail \

-e INFERRAIL_RECEIPTS_PATH=/absolute/path/to/inferrail-receipts.jsonl \ -- uvx --with "mcp>=2.0" inferrail mcp

Two read-only tools, get_spend (known cost and tokens by provider, model, route or any tag such as work_id) and get_health. They don't run inference, write files or pay for anything. Other clients: docs/integrations.md.

How it works

The gateway routes each request by model to a configured provider, checks any budget in scope, forwards the call, and writes one receipt per request, including requests a budget refused. A cost is recorded only when the provider reports usage and a price is on file; otherwise it's unknown, never a guessed $0.

WhereWhat it sees or keeps
Your providerThe full request, exactly as it would without Inferrail, under the provider's own policies.
The gateway, in memoryYour provider key (from its own environment) and the prompts and responses it forwards.
Receipts, on your diskProvider, model, token usage, the price used and the cost, status, timing, and the attribution tags you send.
Not in receiptsPrompt and response bodies.

Attribution tags are stored as sent, so keep secrets and message content out of them. inferrail verify-payload-free prints the live receipt schema, and canary tests check that message bodies never reach receipts; neither is a security audit. The optional usage beacon sends nothing unless an endpoint is configured (details). Architecture: docs/ARCHITECTURE.md.

Current support

  • Endpoints: POST /v1/chat/completions (OpenAI-compatible) and POST /v1/messages (Anthropic-compatible), with streaming and tool calls, plus GET /v1/models.
  • Providers: OpenAI, Anthropic, and endpoints compatible with either. Built-in prices cover OpenAI and Anthropic models; others need a price in your config, or their cost stays unknown.
  • Not supported: the OpenAI Responses API, embeddings, image generation, audio and Realtime, batch, and native Gemini or Bedrock APIs. Fields the gateway can't account for are rejected with a clear error, never silently dropped.
  • Scope: only calls through a running gateway are counted.

Exact contract and non-goals: docs/PRODUCT.md. Running agent jobs that spend real money? Open a production setup request.

Documentation

  • Give one AI agent run a dollar budget
  • Integrations: SDKs, frameworks, attribution, MCP, voice
  • Self-hosting: install, configuration, storage, budgets, dashboard
  • Product scope and architecture
  • Machine-readable: llms.txt, openapi.json (the gateway), config.schema.json

Contributing, security, license

Questions and bugs: GitHub issues. Security or privacy vulnerabilities: report privately, as described in SECURITY.md. Contributing: CONTRIBUTING.md (pytest needs no API key or network). License: Apache-2.0.

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