promptLABSCO SUMMARY
Ask several models, then make one of them decide.
Fanning a prompt out is the easy half. The board-and-decision tool is the reason to reach for it: several models answer, one is nominated to weigh them, and the reasoning is saved with the answers.
What it is
A single interface to several LLM providers. Models are named with a provider prefix, so one call fans out across as many as you list; the responses come back together, can be written out as markdown files, and can be arbitrated by a model you designate.
What you get
- One prompt sent to several models at once, each named by its provider prefix
- The prompt read from a file, for anything longer than a chat message
- Responses written out as markdown files, one per model, into a directory you name
- A board of models answering and a designated model weighing them, with the decision saved alongside the responses
- The available providers listed, and the models available within one of them
- OpenAI, Anthropic, Google Gemini, Groq, DeepSeek and Ollama behind the same call
Requirements
An API key for each provider you intend to call, in the server's environment — Ollama runs locally and needs none. The file-based tools take absolute paths only.
Setup effort
Build from source — clone the repository, then follow the steps on this page and add the configuration for your client
USE CASES
Getting more than one opinion on a hard decision
Several models answer, then the model you nominate reads them and commits to a call — with its reasoning written out.
Comparing how different models handle the same prompt
One call, several providers, answers side by side rather than in four browser tabs.
Keeping the answers for later
Each response written to its own markdown file in a directory you choose.
Running a long prompt you have been editing in a file
The prompt is read from disk, so iterating on it does not mean retyping it.
Finding out what you can actually call
The providers configured, and the models each one exposes.
6 tools
Listed in project docs. Your client may show a different list.
prompt_from_fileprompt_from_file_to_fileceo_and_boardlist_providerslist_modelsBEFORE YOU INSTALL
What to know first
Tools you get
6 tools named in the project's documentation. prompt_from_file_to_file and ceo_and_board write files to abs_output_dir, which defaults to the directory the client launched in. A missing provider key removes a provider, not a tool.
Requires
uv on PATH
INSTALLATION
Connect your client
Clone the repository, then follow the steps on this page and add the configuration for your client. This one is built from source rather than installed with a single command.
Server setup
Setup
git clone https://github.com/disler/just-prompt.git
cd just-prompt
uv sync
cp .env.sample .envEdit the .env file to add your API keys (or export them in your shell): OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY, GROQ_API_KEY, DEEPSEEK_API_KEY, OLLAMA_HOST
Register with a client: use the repo's .mcp.json as-is, or run claude mcp add just-prompt -s project -- uv --directory . run just-prompt (with the path to the just-prompt directory as --directory instead of .)
uv
uv --directory . run just-prompt --default-models openai:o3:high,openai:o4-mini:high,anthropic:claude-opus-4-20250514,anthropic:claude-sonnet-4-20250514,gemini:gemini-2.5-pro-preview-03-25,gemini:gemini-2.5-flash-preview-04-17MCP Clients
Claude Code — repository
{
"mcpServers": {
"just-prompt": {
"type": "stdio",
"command": "uv",
"args": [
"--directory",
".",
"run",
"just-prompt",
"--default-models",
"openai:o3:high,openai:o4-mini:high,anthropic:claude-opus-4-20250514,anthropic:claude-sonnet-4-20250514,gemini:gemini-2.5-pro-preview-03-25,gemini:gemini-2.5-flash-preview-04-17"
],
"env": {}
}
}
}Claude Code — default models
claude mcp add just-prompt -s project \
-- \
uv --directory . \
run just-promptClaude Code — custom model
claude mcp add just-prompt -s project \
-- \
uv --directory . \
run just-prompt --default-models "openai:gpt-4o"