gut

Name: gut
Author: kungie

@kungieMCP server · External

Judgment calls on small, cheap models: is this spam, which team, how urgent. YES, NO or UNSURE.

LLM access & routingv0.8.1Apache-2.0Python

Freeno charge per call

How to install

Runs on your machine from its package; the steps are below.

What it does

Reads
Writes
High impact

At most: only reads or looks up information.

What it costs

Free

Odel charges nothing per call. The publisher may charge for its own service.

Runs locally over stdio

This server isn't hosted — your MCP client launches it from a package registry. Use one of the commands below, or drop the config into your client (e.g. Claude Desktop).

PyPIgutfeel-mcpv0.8.1

Run

uvx gutfeel-mcp

MCP client config

{
  "mcpServers": {
    "gut": {
      "command": "uvx",
      "args": [
        "gutfeel-mcp"
      ]
    }
  }
}

README3 min readView on GitHub

gut

Judgment calls as one line of Python — built for Jev, and running on any small model.

Try it in your browser → The site runs gut's local model in the page: no key, no server.

Your code keeps running into questions that aren't logic: Is this comment spam? Which team owns this ticket? How urgent is it? Is the agent's task done? Until now there were three answers:

Regex and keyword rules — free and instant, and wrong the moment someone phrases it differently.
A frontier LLM — understands anything, at seconds and cents a call, with prose to parse.
Train a classifier — cheap to run, once you have the labelled data, the pipeline and the week.

There is a fourth: a small model made for exactly these questions. TypeSafe AI's Jev answers typed questions directly — a probability for yes, a distribution over options, a score on a scale — with nothing to generate or parse, billed on input only. gut is built around it, and makes it a line of code:

python

import gut

gut.configure(backend=gut.JevBackend())   # or just set TYPESAFE_API_KEY

if gut.likely(comment, "is spam"):
    hide(comment)

No prompt, no parsing, no threshold — and no model named at the call site.

Three questions

python

gut.likely(ticket, "is a bug report")                      # yes / no
gut.classify(ticket, Team)                                 # which one — an Enum
gut.rate(ticket, ["can wait", "this week", "right now"])   # how much

It knows when it doesn't know

A regex never hesitates, and neither does an LLM. gut can:

python

match gut.likely(email, "the customer threatens to cancel", ask_human=True):
    case gut.YES:    escalate(email)
    case gut.NO:     auto_reply(email)
    case gut.UNSURE: send_to_a_person(email)

Say how careful to be in words — lean="yes", stakes="high" — and gut works out the thresholds.

A thousand subjects, one line

python

spam = gut.each(comments).likely("is spam")     # one decision per comment, in order
teams = gut.each(tickets).classify(Team)

Jev gets concurrent requests, a local model batched passes, and nothing already cached is asked twice. @gut.semantic does the same for several questions about one subject.

Jev first, any model

Jev is the model gut is designed around. It is not the only one: the model is configuration, and the same line runs unchanged on any of these.

python

gut.configure(backend=gut.JevBackend())                            # TypeSafe AI's Jev
gut.configure(backend=gut.JevBackend.openrouter())                 # Jev, through OpenRouter
gut.configure(backend=gut.JevBackend.ollaya("winnow:e4b"))         # open decision model, Ollaya
gut.configure(backend=gut.ZeroShotBackend())                       # NLI model, on your CPU
gut.configure(backend=gut.TransformersBackend("Qwen/Qwen3-0.6B"))  # small LLM, on your machine
gut.configure(backend=gut.OpenAICompatibleBackend(                 # Ollama, vLLM, llama.cpp
    "qwen2.5:1.5b", base_url="http://localhost:11434/v1"))
gut.configure(backend=gut.OpenAICompatibleBackend("gpt-4.1-nano")) # OpenAI
gut.configure(backend=gut.OpenAIDecisionsBackend())                # OpenAI's Decisions API

Or several at once. Cascade asks the cheapest model first and passes on only what it is unsure of:

python

gut.configure(backend=gut.Cascade(
    gut.ZeroShotBackend(),   # free and local: settles the obvious
    gut.JevBackend(),        # sees only what the first could not
))

Every answer is a model's own probabilities, never parsed from text, and decision.model names the model that gave it. Your own model can be a backend too: here is how.

Install

bash

pip install "gutfeel[jev]"           # + JevBackend: TypeSafe, OpenRouter or Ollaya
pip install "gutfeel[local]"         # + ZeroShotBackend and TransformersBackend (PyTorch)
pip install gutfeel                  # core: any OpenAI-compatible server; FakeBackend for tests
pip install gutfeel-mcp             # + the MCP server, gutfeel-mcp

The package on PyPI is gutfeel (gut was taken); the import is plain import gut. No model at hand? gut.FakeBackend(answers={"is spam": 0.97}) answers from fixtures, for tests.

From a shell, and for agents

The big model thinks; the small one decides, fast. An agent with gut judges a thousand files, commits or search results in one command instead of reading each one itself:

bash

git ls-files | gut filter "retries failed requests" --read-files --max-cost 0.50
claude mcp add gut --env TYPESAFE_API_KEY=your-key -- uvx gutfeel-mcp   # MCP Registry: io.github.Kungie/gut
npx skills add Kungie/gut --skill gut     # teaches a coding agent when to reach for it

Docs

The documentation, one page per idea: Getting started · Backends · Knowing when it doesn't know · Asking everything at once · Async · Exact costs · Caching and observability · Command line · MCP server · Honest limitations. examples/ runs the same code on every backend.

Status and license

Pre-1.0, Apache-2.0. Every code block in these docs runs in the test suite · contributing

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