MCP server de focxle
✓ VerificadoRastrea y atribuye los costos de agentes de IA de múltiples proveedores monitoreando el uso de tokens directamente en tu proceso.
También conocido como focxle/focxle-python, focxle-python, focxle
- Stars
- 0
- Herramientas
- —
- Descargas, 30 días
- 122
- Última versión
- 0.1.7
- Último commit
- 10 ago 2026
- Licencia
- MIT
¿Qué es focxle MCP?
focxle es un MCP server. Rastrea y atribuye los costos de agentes de IA de múltiples proveedores monitoreando el uso de tokens directamente en tu proceso.
Resumen generado a partir del README del proyecto
Acerca del MCP server de focxle
focxle ofrece una forma de rastrear y atribuir los costos de agentes de IA de distintos proveedores dentro de un mismo proceso. En lugar de revisar los dashboards individuales de OpenAI, Anthropic o Serper, los desarrolladores pueden ver un desglose unificado del gasto atribuido a agentes específicos. Funciona monitoreando el uso de tokens directamente desde los conteos del propio proveedor, lo que garantiza la precisión sin depender de estimaciones o proxies.
La herramienta está diseñada para una integración local mediante una librería de Python que no requiere cuenta, ni API keys, ni llamadas de red iniciales. Está construida para fallar de forma abierta; es decir, si la herramienta de medición encuentra un error, las peticiones de tu agente continuarán sin problemas. Para los desarrolladores que utilizan editores con IA integrada como Cursor, Windsurf o Claude Code, el MCP server incluido permite consultar el historial de gastos directamente a través de la interfaz de chat mediante un resumen local en JSON.
Qué puedes hacer con él
- 1Analizar costos de agentes multi-proveedorPide a tu editor de IA que calcule el gasto total de todos los proveedores de API utilizados por tus agentes esta semana.
- 2Identificar flujos de trabajo de agentes costososDetermina qué nombre de agente específico es responsable de la mayor parte de tu factura actual de API.
- 3Simular límites de presupuesto para ahorrar costosEvalúa cuánto dinero se habría ahorrado si hubieras implementado un límite de gasto específico por llamada.
Escrito a partir del README del proyecto. Cada caso de uso se verifica con una cita del mismo.
Cómo instalar focxle MCP
Formatos verificados el 10 nov 2026Añadir a CursorAñadir a GooseInstalar focxle MCP en Claude Code
$ claude mcp add focxle-python -- uvx focxleInstalar focxle MCP en Cursor
~/.cursor/mcp.json
{
"mcpServers": {
"focxle-python": {
"command": "uvx",
"args": [
"focxle"
]
}
}
}Instalar focxle MCP en VS Code
.vscode/mcp.json
{
"servers": {
"focxle-python": {
"type": "stdio",
"command": "uvx",
"args": [
"focxle"
]
}
}
}Instalar focxle MCP en VS Code CLI
$ code --add-mcp '{"name":"focxle-python","type":"stdio","command":"uvx","args":["focxle"]}'Instalar focxle MCP en Claude Desktop
claude_desktop_config.json
{
"mcpServers": {
"focxle-python": {
"command": "uvx",
"args": [
"focxle"
]
}
}
}Instalar focxle MCP en Codex
$ codex mcp add focxle-python -- uvx focxleInstalar focxle MCP en Codex config.toml
~/.codex/config.toml
[mcp_servers.focxle-python] command = "uvx" args = ["focxle"]
Instalar focxle MCP en Gemini CLI
$ gemini mcp add -s user focxle-python uvx -- focxleInstalar focxle MCP en Gemini settings.json
~/.gemini/settings.json
{
"mcpServers": {
"focxle-python": {
"command": "uvx",
"args": [
"focxle"
]
}
}
}Configuración
FOCXLE_NO_HISTORYSet to 1 to stop focxle keeping any local spend history. The server then has nothing to read and will say so.
| Nombre | Descripción | Tipo |
|---|---|---|
| FOCXLE_NO_HISTORY | Set to 1 to stop focxle keeping any local spend history. The server then has nothing to read and will say so. |
Verificaciones automáticas
Ejecutado 15 nov 2026Informe completo →Pasó nuestras verificaciones automáticas. Esto no es una auditoría de seguridad: lee el código y dale al servidor el acceso más limitado que necesite. Cómo funcionan las verificaciones
- Repositorio de origen vinculado y accesiblefocxle/focxle-python is public and reachablegithub.com/focxle/focxle-python ↗Aprobado
- El paquete indica este repositorio como su origenfocxle names focxle/focxle-python as sourcepypi.org/project/focxle/ ↗Aprobado
- Procedencia de la compilación (atestación de Sigstore)No build attestation published for this releaseNo verificado
- Licencia de código abierto (OSI)MITspdx.org/licenses/MIT.html ↗Aprobado
- Commit o lanzamiento en el último añoLast commit or release 2026-09-21github.com/focxle/focxle-python ↗Aprobado
- OpenSSF ScorecardNo verificado
- El nombre del paquete no es similar a un paquete popularNo popular package one edit away from focxleAprobado
- No hay avisos críticos en el paquete ni en sus dependencias1 resolved dependencies checked; no critical advisoryosv.dev ↗Aprobado
- Sin instrucciones ocultas para el modelo0 tool descriptions and the README read; no override phrases, credential paths or hidden charactersAprobado
- Las herramientas destructivas están etiquetadasNo verificado
- El endpoint remoto responde al handshake de MCPNo verificado
README
Ver en GitHubfocxle
See what every AI agent costs you, across every vendor. In one line, with no account, in under a second.
Nothing you already have can tell you what one agent cost. Your OpenAI dashboard cannot see Anthropic. Neither can see Serper, Firecrawl, or the twelve other APIs your agent calls in a loop at 3am. They all report per key and per month, which is the wrong unit twice over: you do not run a key, you run agents, and by the time the month closes the money is gone.
focxle runs inside your process, so it sees every one of them, attributes them to the agent that made the call, and prices them from the vendor's own token counts rather than a guess. Connect an account and payments your agent settles on-chain land in the same total.
Why the number gets large fast. Do the arithmetic on a stuck agent. Two frontier-model calls a second, 10k tokens in and 1k out, at $5 per million in and $25 per million out, is 7.5 cents a call and $540 an hour. Twelve hours overnight is $6,480. A two-day weekend is $25,900. Nobody budgets for this, because nobody plans it: it is a bad stopping condition, one retry wrapper too many, a tool that feeds its own output back into the prompt. The first anyone hears is the invoice.
That is the entire reason this exists. It is free, forever, to watch it happen and to see exactly what a cap would have stopped before you pay for one.
This library fails open. It runs inside your agent, so the worst thing it
could do is break a request it was only supposed to measure. If anything inside
it fails, your call goes through and the failure is counted and printed at the
end. focxle.init(fail_open=False) inverts that if an unmeasured call is
genuinely worse for you than a failed one.
It blocks nothing by default. Out of the box it only measures. Refusing a call needs an account on a paid plan, and even then the instruction comes from a signed document rather than a local setting, so nothing you or we write in code can quietly switch it on. Calls to vendors we cannot price are always allowed through.
pip install focxle
import focxle
focxle.init()
That is the whole setup. No account, no API key, no config file, and no network call. Prices ship with the package, so the first run works on a plane. Telemetry is off unless you turn it on, and we ask exactly once.
What you get
focxle spend $47.21
--------------------------------------------------------
support-triage $31.04 (1,284 calls)
anthropic $28.10
google.serper.dev $2.94
research $16.17 (203 calls)
openai $14.02
api.firecrawl.dev $2.15
3 calls would have been stopped under $1.50/call and $50/day
$12.30 prevented, worst single call $4.10
3x single call $4.10 over the $1.50 per-call limit
$5,904 a month at the rate this run was going
$412.60 prevented across 17 runs in 9 days
observe mode: nothing was actually blocked
prices dated 2026-08-05
turn these into real caps: https://focxle.com/upgrade
The second block is the point. It is what a cap would have done, evaluated by the same code that would do the blocking, printed so you can decide whether you want one before you pay for one.
Every figure there is a measurement, not a sales number. The total, the worst call and the running total are sums of calls that really happened, priced from the vendor's own usage numbers. The monthly figure is the only projection, it is this run's own measured rate carried forward, it says so on the line itself, and it is withheld entirely unless the run was long enough and busy enough to mean anything.
If a cap would have stopped a large share of your calls, it says that instead of quoting a figure. A cap that blocks a quarter of an agent's work is not saving money, it is stopping the agent, and calling the difference a saving would make the number worthless on the day it matters. Nothing is offered for sale on those runs either.
Why there is nothing else quite like this
Cost tools for AI fall into three shapes, and all three ask you for something before they tell you anything.
Vendor dashboards are per key and per month, one per vendor, and none of them can see the others. Four vendors means four tabs and no total.
Proxies and gateways see everything, because every call now goes through somebody else's server. That is a new dependency on your live path, a new company holding your prompts, and a new thing that can be down at 3am.
Observability platforms want a trace pipeline, an account, and a seat or per-trace bill before the first number appears. Several are excellent. All of them are a project, and none of them is something you do at 4pm because you got a surprising invoice at lunchtime.
focxle is pip install focxle and one line. It has zero dependencies, so
nothing enters your environment and nothing can conflict with your pins. It
makes zero network calls on the free path, which the test suite proves by
deleting socket.socket and running the whole suite anyway. It needs no
account, no key and no config file, and the price table ships inside the
package, so the first run works on a plane. It fails open by construction:
the worst thing a bug in here can do is fail to measure a call that still went
through.
And it answers the question none of the others can: what did this agent cost me, across everything it touched.
Attributing spend to an agent
Per agent is the number nobody else can show you, and it only exists if calls are labelled.
with focxle.agent("support-triage"):
result = my_agent.run(ticket)
Works across threads and async tasks. Or set FOCXLE_AGENT in the environment,
which is usually right, because the same image is normally deployed as several
different agents.
Exact token counts
The HTTP layer already sees your model calls. Wrapping the client upgrades them from "a request happened" to the vendor's own token counts.
from openai import OpenAI
client = focxle.watch(OpenAI())
Streaming is handled. Wrapping the same client twice is a no-op rather than a double count.
For LangChain, where the framework builds the client for you:
chain.invoke(x, config={"callbacks": [focxle.langchain_handler()]})
Ask it in your editor
focxle ships an MCP server, so you can ask about your own spend in Claude Code, Cursor, Windsurf, Zed or anything else that speaks MCP:
what did my agents cost this week which agent is the expensive one what would a $2 per call cap have stopped
Add this to your client's MCP config:
{
"mcpServers": {
"focxle": { "command": "python", "args": ["-m", "focxle", "mcp"] }
}
}
It answers from a small daily rollup kept in ~/.focxle/history.json, written
when an instrumented process exits. It reads that file and nothing else: no
network, no account, and it never starts measuring anything itself, so your
editor's own model calls are never counted as yours. FOCXLE_NO_HISTORY=1
turns the rollup off, and then there is nothing to ask about.
Reading the numbers yourself
focxle.spend() # total USD observed
focxle.spend("support-triage") # for one agent
focxle.counterfactual() # what a cap would have stopped
focxle.report() # print now instead of at exit
focxle.init(file="~/spend.jsonl") # one JSON object per call
What it does not do
It does not block anything on its own. With no account, policy evaluation
runs and the verdict is only recorded, so you see what a cap would have done
before you decide you want one. Policy(mode="enforce") written in your own
code is refused and downgraded to observe, with a note on stderr: enforcement
is something an account is granted, not a flag a process sets for itself.
When it is granted, caps are judged on what you have actually spent, not on a
guess at what a call is about to cost. Output length cannot be known before the
reply is written, so guessing it refuses ordinary work and misses the runaways.
The one exception is the per-call ceiling, which has to be decided in advance:
it prices the max_tokens you asked for, so set it above what your largest
declared reply would cost rather than above what calls typically cost.
It does not price everything. A vendor missing from the price table is recorded at zero, and both the host and the exact model id are named in the report, so a zero that means "free" is never confused with a zero that means "we did not recognise this". The call is allowed through. Blocking a call because we do not know what it costs would be this library making a decision on missing information inside a process that trusted it to be passive. An invented figure would be worse, because it looks like knowledge.
Routers are priced as the provider they name, so a call through OpenRouter to
anthropic/claude-haiku-4.5 costs what that model costs. Dots and dashes in a
version are treated as one spelling, because which one you see depends on whose
SDK produced the string.
It is per process. An agent running across fifty workers produces fifty
partial pictures. Reconciling them needs somewhere to send them, which is what
an account is for. The one exception is the running total of what a cap would
have prevented, which is kept in ~/.focxle/lifetime.json so a second run does
not start from zero. It holds two numbers and a date, never leaves your
machine, and FOCXLE_NO_LIFETIME=1 stops it being written at all.
Everything under ~/.focxle is created for your user only, 0700 on the
directory and 0600 on the files, the way ~/.ssh and ~/.aws/credentials
are. Your account id is a credential: whoever reads it can read that account's
spend and start a checkout against it, and agent workloads run on shared CI
runners constantly. Anything an earlier version left world readable is
tightened the next time it is written.
It has no dependencies. Nothing is added to your environment and nothing can conflict with your versions. The SDKs and frameworks it instruments are detected at runtime and never imported at install time.
What is sent anywhere
Nothing, unless you turn it on. There is no network call on the observe path at
all, which the test suite asserts by removing socket.socket and running
anyway.
There is exactly one thing we ask for, printed once after your first breakdown and never again. It is off until you type a command:
python -m focxle share preview # the exact JSON, before you decide
python -m focxle share on
python -m focxle share off
If you turn it on, it sends anonymous vendor totals: which vendors you used, how many calls, how much, and the hosts we could not price. That last one is the whole reason we ask, because it is how we know what to price next.
It never sends your agent names, your model names, prompts, responses, URLs or
anything derived from your machine. The install id is a random UUID generated
locally, not a hash of your hostname, because anything derived can be reversed
by whoever holds the same input. Private hosts (.internal, .corp, bare
names, IP addresses) are dropped from every field and counted under withheld,
so the totals are visibly partial rather than quietly so.
It never prompts. This runs at process exit, often with no terminal attached, and blocking on stdin there would hang a production agent.
If you enable the local file sink, it records the host and the first two segments of the path, never query strings, so your search terms and document ids stay out of it. That file never leaves your machine.
Compatibility
Python 3.9 and up. Instruments openai, anthropic, httpx, requests and
LangChain when they are present, and does nothing when they are not.
Source, issues and contributions
The source is published at github.com/focxle/focxle-python so you can read it before running it inside your agent. It is generated from a private build repository and is source only: the test suite lives there and is not mirrored here, because those files have never been through the checks that decide what is safe to publish.
Because the repo is generated, pull requests cannot be merged back and are not accepted. That is a limitation of how this is built, not a comment on your patch. What is genuinely wanted, and acted on:
- Bugs and feature requests — open an issue, or email mrinal@focxle.com.
- A vendor or model we price wrongly, or do not price at all. The report names them, and that list is the single most useful thing you can send us.
- Security findings — use GitHub's private vulnerability reporting on that repo, or email the address above. Please give us a reasonable window before publishing.
Licence
MIT.
Preguntas sobre focxle MCP
¿Cómo agrego focxle MCP a Claude Code?
Ejecuta claude mcp add focxle-python -- uvx focxle en tu terminal. Añade --scope user para que esté disponible en todos los proyectos.
¿Cómo agrego focxle MCP a Cursor?
Abre ~/.cursor/mcp.json y añade el servidor en mcpServers, usando la configuración de Cursor que se muestra en la sección de instalación de arriba.
¿Requiere focxle una cuenta o una API key para empezar?
No, no requiere cuenta, ni API key, ni archivos de configuración para funcionar. Los precios están incluidos en el paquete, por lo que funciona sin conexión.
¿El uso de focxle romperá mi código de agente actual?
La librería está diseñada para fallar de forma abierta (fail open), lo que significa que si ocurre un error, tu llamada se procesará de todos modos y el fallo simplemente se registrará.
¿Puedo usar focxle con mi editor de código?
Sí, incluye un MCP server que te permite consultar el gasto de tus agentes en editores como Claude Code, Cursor, Windsurf o Zed.
¿Envía mis prompts o respuestas a un servidor?
No, no envía nombres de agentes, nombres de modelos, prompts, respuestas ni URLs. La telemetría está desactivada por defecto y solo envía totales anónimos por proveedor si la habilitas explícitamente.