Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Runs on your computer
Inferrail
Overview
Description from the maker (original English)
Local, payload-free AI cost visibility for agents. Query spend by model, route, or tagged work.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiVery new↓ 332/wk · ★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Other
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- You fill in yourself:
INFERRAIL_RECEIPTS_PATH
You need
- PyPIPython’s uv installed (for the uvx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"inferrail": {
"command": "uvx",
"args": [
"inferrail",
"mcp"
],
"env": {
"INFERRAIL_RECEIPTS_PATH": "your-value"
}
}
}
}Run this in your terminal
claude mcp add inferrail --env INFERRAIL_RECEIPTS_PATH=your-value -- uvx inferrail mcpFill in the empty values before running it.
Or copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"inferrail": {
"command": "uvx",
"args": [
"inferrail",
"mcp"
],
"env": {
"INFERRAIL_RECEIPTS_PATH": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"inferrail": {
"type": "stdio",
"command": "uvx",
"args": [
"inferrail",
"mcp"
],
"env": {
"INFERRAIL_RECEIPTS_PATH": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
Values you fill in
INFERRAIL_RECEIPTS_PATHenvAbsolute path to your Inferrail receipts file (JSONL or SQLite). Used when a tool call gives no receipts_path. Default: ./inferrail-receipts.jsonl
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account domondi1, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Otheri
- More stars than 0% of the other 18499 servers in this category
- Published to the registry more recently than 87% of the other 18499 servers in this category
- Basic checks equal to or better than 100% of the other 18499 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is Inferrail safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
The registry does not list a required key, though some servers ask you to sign in on first use.
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed –, and the latest version (0.4.18) reached the registry 6 days ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
What can I use instead?
In the same category: Spec Driven Development, Remote Desktop Commander, SolveHub
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Remote Desktop Commander
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Runs in the cloud✓ desktopcommander.app
SolveHub
CAPTCHA and anti-bot solving for Turnstile, DataDome, Akamai, PerimeterX and Imperva.
Runs on your computer
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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.
| Capability | What it does | Where | Status |
|---|---|---|---|
| Gateway | OpenAI- 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 inferrail | Available |
| Cost receipts | One record per call: tokens, price, cost, status, your tags. Never prompts or responses. Totals by job, customer or any tag. | local SQLite or JSONL | Available |
| MCP server | Read-only get_spend and get_health tools over your local receipts. | inferrail mcp | Preview |
| Inferrail Hosted | The 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 guide | Preview |
| Web Read | One public web page as clean markdown with a receipt, paid per call by the agent over x402 or MPP. | below | Preview |
| Agent purchases under a job budget | One budget covering model calls and x402 purchases; a purchase that doesn't fit is refused before it's signed. Local proof, no mainnet. | example | Experimental |
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 inferrailinferrail 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 inferrailfrom 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.
| Where | What it sees or keeps |
|---|---|
| Your provider | The full request, exactly as it would without Inferrail, under the provider's own policies. |
| The gateway, in memory | Your provider key (from its own environment) and the prompts and responses it forwards. |
| Receipts, on your disk | Provider, model, token usage, the price used and the cost, status, timing, and the attribution tags you send. |
| Not in receipts | Prompt 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) andPOST /v1/messages(Anthropic-compatible), with streaming and tool calls, plusGET /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.