Just Prompt - A lightweight MCP server for LLM providers

just-prompt is a Model Control Protocol (MCP) server that provides a unified interface to various Large Language Model (LLM) providers including OpenAI, Anthropic, Google Gemini, Groq, DeepSeek, and Ollama.

Tools

The following MCP tools are available in the server:

prompt: Send a prompt to multiple LLM models
- Parameters:
  - text: The prompt text
  - models_prefixed_by_provider (optional): List of models with provider prefixes. If not provided, uses default models.
prompt_from_file: Send a prompt from a file to multiple LLM models
- Parameters:
  - file: Path to the file containing the prompt
  - models_prefixed_by_provider (optional): List of models with provider prefixes. If not provided, uses default models.
prompt_from_file_to_file: Send a prompt from a file to multiple LLM models and save responses as markdown files
- Parameters:
  - file: Path to the file containing the prompt
  - models_prefixed_by_provider (optional): List of models with provider prefixes. If not provided, uses default models.
  - output_dir (default: "."): Directory to save the response markdown files to
list_providers: List all available LLM providers
- Parameters: None
list_models: List all available models for a specific LLM provider
- Parameters:
  - provider: Provider to list models for (e.g., 'openai' or 'o')

Provider Prefixes

every model must be prefixed with the provider name

use the short name for faster referencing

o or openai: OpenAI
- o:gpt-4o-mini
- openai:gpt-4o-mini
a or anthropic: Anthropic
- a:claude-3-5-haiku
- anthropic:claude-3-5-haiku
g or gemini: Google Gemini
- g:gemini-2.5-pro-exp-03-25
- gemini:gemini:gemini-2.5-pro-exp-03-25
q or groq: Groq
- q:llama-3.1-70b-versatile
- groq:llama-3.1-70b-versatile
d or deepseek: DeepSeek
- d:deepseek-coder
- deepseek:deepseek-coder
l or ollama: Ollama
- l:llama3.1
- ollama:llama3.1

Features

Unified API for multiple LLM providers
Support for text prompts from strings or files
Run multiple models in parallel
Automatic model name correction using the first model in the --default-models list
Ability to save responses to files
Easy listing of available providers and models

Installation

# Clone the repository
git clone https://github.com/yourusername/just-prompt.git
cd just-prompt

# Install with pip
uv sync

Environment Variables

Create a .env file with your API keys (you can copy the .env.sample file):

cp .env.sample .env

Then edit the .env file to add your API keys (or export them in your shell):

OPENAI_API_KEY=your_openai_api_key_here
ANTHROPIC_API_KEY=your_anthropic_api_key_here
GEMINI_API_KEY=your_gemini_api_key_here
GROQ_API_KEY=your_groq_api_key_here
DEEPSEEK_API_KEY=your_deepseek_api_key_here
OLLAMA_HOST=http://localhost:11434

Claude Code Installation

Default model set to anthropic:claude-3-7-sonnet-20250219.

If you use Claude Code right out of the repository you can see in the .mcp.json file we set the default models to...

{
  "mcpServers": {
    "just-prompt": {
      "type": "stdio",
      "command": "uv",
      "args": [
        "--directory",
        ".",
        "run",
        "just-prompt",
        "--default-models",
        "anthropic:claude-3-7-sonnet-20250219,openai:o3-mini,gemini:gemini-2.5-pro-exp-03-25"
      ],
      "env": {}
    }
  }
}

The --default-models parameter sets the models to use when none are explicitly provided to the API endpoints. The first model in the list is also used for model name correction when needed. This can be a list of models separated by commas.

When starting the server, it will automatically check which API keys are available in your environment and inform you which providers you can use. If a key is missing, the provider will be listed as unavailable, but the server will still start and can be used with the providers that are available.

Using `mcp add-json`

Copy this and paste it into claude code with BUT don't run until you copy the json

claude mcp add just-prompt "$(pbpaste)"

JSON to copy

{
    "command": "uv",
    "args": ["--directory", ".", "run", "just-prompt"]
}

With a custom default model set to openai:gpt-4o.

{
    "command": "uv",
    "args": ["--directory", ".", "run", "just-prompt", "--default-models", "openai:gpt-4o"]
}

With multiple default models:

{
    "command": "uv",
    "args": ["--directory", ".", "run", "just-prompt", "--default-models", "anthropic:claude-3-7-sonnet-20250219,openai:gpt-4o,gemini:gemini-2.5-pro-exp-03-25"]
}

Using `mcp add` with project scope

# With default model (anthropic:claude-3-7-sonnet-20250219)
claude mcp add just-prompt -s project \
  -- \
    uv --directory . \
    run just-prompt

# With custom default model
claude mcp add just-prompt -s project \
  -- \
  uv --directory . \
  run just-prompt --default-models "openai:gpt-4o"

# With multiple default models
claude mcp add just-prompt -s user \
  -- \
  uv --directory . \
  run just-prompt --default-models "anthropic:claude-3-7-sonnet-20250219:4k,openai:o3-mini,gemini:gemini-2.0-flash,openai:gpt-4.5-preview,gemini:gemini-2.5-pro-exp-03-25"

`mcp remove`

claude mcp remove just-prompt

Running Tests

uv run pytest

Codebase Structure

.
├── ai_docs/                   # Documentation for AI model details
│   ├── llm_providers_details.xml
│   └── pocket-pick-mcp-server-example.xml
├── list_models.py             # Script to list available LLM models
├── pyproject.toml             # Python project configuration
├── specs/                     # Project specifications
│   └── init-just-prompt.md
├── src/                       # Source code directory
│   └── just_prompt/
│       ├── __init__.py
│       ├── __main__.py
│       ├── atoms/             # Core components
│       │   ├── llm_providers/ # Individual provider implementations
│       │   │   ├── anthropic.py
│       │   │   ├── deepseek.py
│       │   │   ├── gemini.py
│       │   │   ├── groq.py
│       │   │   ├── ollama.py
│       │   │   └── openai.py
│       │   └── shared/        # Shared utilities and data types
│       │       ├── data_types.py
│       │       ├── model_router.py
│       │       ├── utils.py
│       │       └── validator.py
│       ├── molecules/         # Higher-level functionality
│       │   ├── list_models.py
│       │   ├── list_providers.py
│       │   ├── prompt.py
│       │   ├── prompt_from_file.py
│       │   └── prompt_from_file_to_file.py
│       ├── server.py          # MCP server implementation
│       └── tests/             # Test directory
│           ├── atoms/         # Tests for atoms
│           │   ├── llm_providers/
│           │   └── shared/
│           └── molecules/     # Tests for molecules

Context Priming

READ README.md, then run git ls-files, and 'eza --git-ignore --tree' to understand the context of the project.

Thinking Tokens with Claude

The Anthropic Claude model claude-3-7-sonnet-20250219 supports extended thinking capabilities using thinking tokens. This allows Claude to do more thorough thought processes before answering.

You can enable thinking tokens by adding a suffix to the model name in this format:

anthropic:claude-3-7-sonnet-20250219:1k - Use 1024 thinking tokens
anthropic:claude-3-7-sonnet-20250219:4k - Use 4096 thinking tokens
anthropic:claude-3-7-sonnet-20250219:8000 - Use 8000 thinking tokens

Example usage:

# Using 4k thinking tokens with Claude
uv run just-prompt prompt "Analyze the advantages and disadvantages of quantum computing vs classical computing" \
  --models-prefixed-by-provider anthropic:claude-3-7-sonnet-20250219:4k

Notes:

Thinking tokens are only supported for the claude-3-7-sonnet-20250219 model
Valid thinking token budgets range from 1024 to 16000
Values outside this range will be automatically adjusted to be within range
You can specify the budget with k notation (1k, 4k, etc.) or with exact numbers (1024, 4096, etc.)

Just Prompt - A lightweight MCP server for LLM providers