Judgment calls on small, cheap models: is this spam, which team, how urgent. YES, NO or UNSURE.
MCP Server: io.github.Kungie/gut
The MCP server io.github.Kungie/gut provides judgment calls on small, cheap models. It assesses whether content is spam, identifies which team it relates to, and determines urgency. Results are expressed as YES, NO, or UNSURE, based on the server’s description.
🛠️ Key Features
Spam determination (YES, NO, UNSURE)
Team identification
Urgency assessment
Binary-style outputs plus uncertainty (YES, NO, UNSURE)
🚀 Use Cases
Classifying whether input is spam
Routing requests to the appropriate team
Prioritizing work based on urgency signals
⚡ Developer Benefits
Clear, constrained decision outputs (YES, NO, UNSURE)
Focused behavior for small/cheap model scenarios
⚠️ Limitations
Described scope centers on judgment calls; no additional tools, integrations, or capabilities are provided in the available data.
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_KEYif 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
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
