Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Descripción del creador (original en inglés)
Local, payload-free AI cost visibility for agents. Query spend by model, route, or tagged work.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
INFERRAIL_RECEIPTS_PATHNecesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"inferrail": {
"command": "uvx",
"args": [
"inferrail",
"mcp"
],
"env": {
"INFERRAIL_RECEIPTS_PATH": "your-value"
}
}
}
}Ejecuta esto en tu terminal
claude mcp add inferrail --env INFERRAIL_RECEIPTS_PATH=your-value -- uvx inferrail mcpCompleta los valores vacíos antes de ejecutarlo.
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"inferrail": {
"command": "uvx",
"args": [
"inferrail",
"mcp"
],
"env": {
"INFERRAIL_RECEIPTS_PATH": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"inferrail": {
"type": "stdio",
"command": "uvx",
"args": [
"inferrail",
"mcp"
],
"env": {
"INFERRAIL_RECEIPTS_PATH": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
INFERRAIL_RECEIPTS_PATH envEsta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 8 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub domondi1, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (0.4.18) llegó al registro hace 5 días.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Python (uv) en tu computadora.
En la misma categoría: Spec Driven Development, Remote Desktop Commander, Tethered Trust
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Fit, compatibility and recall checks for AI agents before they buy: verdicts, evidence, receipts.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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
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).
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.
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/"}'
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.
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.
POST /v1/chat/completions (OpenAI-compatible) and POST /v1/messages (Anthropic-compatible), with streaming and tool calls, plus GET /v1/models.Exact contract and non-goals: docs/PRODUCT.md. Running agent jobs that spend real money? Open a production setup request.
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.