DecisionLint: Jev/Luna Lint

DecisionLint: Jev/Luna Lint

Lint Jev and Luna decision-model questions before you send them

JSON, JavaScript, TypeScript, Python, Rust and Go, for TypeSafe, OpenAI and every platform that serves either

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What it does

Lint Jev and Luna questions before you send them. DecisionLint checks the questions you write for a decision model: TypeSafe's Jev, OpenAI's Decisions API (gpt-6-luna), and every platform that serves either shape, from OpenRouter and Vercel AI Gateway to a llama.cpp on your own machine. DecisionLint reads them out of your JSON and your code, and reports the ones written in a way that is measured to produce bad answers. Linting makes no API calls and needs no key. One optional command asks the model itself to check a file, using your own key, at whichever platform you send it to.

• Lints as you type in JSON, JavaScript, TypeScript, Python, Rust and Go.
• Fixes on the lightbulb for the mechanical mistakes, and a way to silence a finding you disagree with.
• The same rules in CI, from a command line, and for AI agents, from an MCP server. One settings file covers all three.
• Every platform, one set of rules. TypeSafe's /v1/systemone shape and OpenAI's /v1/decisions shape, predicate, choices and levels, are read into the same rules, and the AI SDK's decide() call for either. The limits a request is held to are the platform's own, read from the model id's spelling, a URL in the file or a target setting: Vercel's 255 choices, LiteLLM's 128 questions, none where a platform publishes none.
• Two optional commands that use your own key: one asks the model whether your questions have problems a text pattern cannot see, and one re-sends a question with its layout changed to see whether the answer holds. Both go to the platform the model id names, or to an endpoint you set, a local llama.cpp included.

A request like this one looks fine and has three problems:

{

"model": "jev-latest", "state": "My parcel came three days late and the lid is cracked.", "questions": { "team": { "type": "choice", "instructions": "Which team should handle this?", "criteria": { "billing": "Charges and refunds", "delivery": "Late or damaged parcels" } }, "late": { "type": "noul", "instructions": "Did more than two parcels arrive late?" } } }

Install

Use it from an AI agent

The same program is an MCP server, so an agent that writes Jev or OpenAI Decisions questions can lint what it wrote and fix it before you see it. The tool descriptions and the server's instructions name both vendors and all three request shapes, so an agent knows to call it for any of them.

It offers eight tools. lint_text lints a request or source code passed as text and lint_paths lints files on disk, with each finding carrying the edit that would mend it. fix_text returns the text with the safe fixes applied and the report of what is left, and writes nothing. list_rules lists every rule with what it means, and explain_rule returns a rule's own page, with an example that is flagged and one that is not. Those send nothing and need no key.

The other three are the editor's Ask commands and Probe for an agent. ask_model asks Jev, or Luna with model: gpt-6-luna, to check the questions in text or in files, and returns the lint report with the model's findings added. plan_ask says what that would send, and sends nothing. probe_question sends one question with its layout varied and reports whether the answer held. The two that send use your key and cost money, and their descriptions tell an agent to call them only when you ask. The key is read from TYPESAFE_API_KEY or OPENAI_API_KEY in the environment the client started the server with, or from .env.local or .env in the directory it started it in, and never from a tool argument, so no key lands in an agent's transcript. Every tool answers as structured content beside the text, and none writes a file.

It is listed on the MCP registry as io.github.nolindnaidoo/decisionlint, for a client that installs servers from there.

Configuring it by hand : any host with an MCP config file

Most hosts read a JSON config. Add one entry:

{

"mcpServers": { "decisionlint": { "command": "npx", "args": ["-y", "decisionlint-mcp"] } } }

-y skips the install prompt on first run. To skip npx on every launch, install it once with npm install -g decisionlint-mcp and set "command": "decisionlint-mcp" instead.

A project that already pins decisionlint for CI has the same server behind npx decisionlint --mcp. The package with no flag is the one to give a client, because decisionlint started bare is a linter that exits, not a server.

What it catches

Jev and Luna guarantee the type of the answer, not the answer. A badly formed question still comes back with a confident-looking number. These rules catch the mistakes that can be read from the text.

| DEC303 | model-overlapping-options | info | Jev reads two opt