Just Prompt (Multi-LLM Provider)

Just Prompt - A lightweight MCP server for LLM providers

just-promptis 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. See how we use theceo_and_boardtool to makehard decisions easy with o3 here.

# Clone the repository git clone https://github.com/yourusername/just-prompt.git cd just-prompt # Install with pip uv sync

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

{ "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": {} } } }

The--default-modelsparameter 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.

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

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

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

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

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

{ "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"] }

# With default models 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 "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"

. ├── ai_docs/ # Documentation for AI model details │ ├── extending_thinking_sonny.md │ ├── llm_providers_details.xml │ ├── openai-reasoning-effort.md │ └── pocket-pick-mcp-server-example.xml ├── example_outputs/ # Example outputs from different models ├── list_models.py # Script to list available LLM models ├── prompts/ # Example prompt files ├── pyproject.toml # Python project configuration ├── specs/ # Project specifications │ ├── init-just-prompt.md │ ├── new-tool-llm-as-a-ceo.md │ └── oai-reasoning-levels.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 │ │ ├── ceo_and_board_prompt.py │ │ ├── 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 │ ├── test_ceo_and_board_prompt.py │ ├── test_list_models.py │ ├── test_list_providers.py │ ├── test_prompt.py │ ├── test_prompt_from_file.py │ └── test_prompt_from_file_to_file.py └── ultra_diff_review/ # Diff review outputs

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

For OpenAI o‑series reasoning models (o4-mini,o3-mini,o3) you can control how muchinternalreasoning the model performs before producing a visible answer.

Append one of the following suffixes to the model name (after theproviderprefix):

- :low– minimal internal reasoning (faster, cheaper)
- :medium– balanced (default if omitted)
- :high– thorough reasoning (slower, more tokens)

When a reasoning suffix is present,just‑promptautomatically switches to the OpenAIResponsesAPI (when available) and sets the correspondingreasoning.effortparameter. If the installed OpenAI SDK is older, it gracefully falls back to the Chat Completions endpoint and embeds an internal system instruction to approximate the requested effort level.

The Anthropic Claude modelsclaude-opus-4-20250514andclaude-sonnet-4-20250514support 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-opus-4-20250514:1k- Use 1024 thinking tokens for Opus 4
- anthropic:claude-sonnet-4-20250514:4k- Use 4096 thinking tokens for Sonnet 4
- anthropic:claude-opus-4-20250514:8000- Use 8000 thinking tokens for Opus 4

- Thinking tokens are supported forclaude-opus-4-20250514,claude-sonnet-4-20250514, andclaude-3-7-sonnet-20250219models
- 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.)

The Google Gemini modelgemini-2.5-flash-preview-04-17supports extended thinking capabilities using thinking budget. This allows Gemini to perform more thorough reasoning before providing a response.

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

- gemini:gemini-2.5-flash-preview-04-17:1k- Use 1024 thinking budget
- gemini:gemini-2.5-flash-preview-04-17:4k- Use 4096 thinking budget
- gemini:gemini-2.5-flash-preview-04-17:8000- Use 8000 thinking budget

- Thinking budget is only supported for thegemini-2.5-flash-preview-04-17model
- Valid thinking budget range from 0 to 24576
- 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.)

- https://docs.anthropic.com/en/api/models-list?q=list+models
- https://github.com/googleapis/python-genai
- https://platform.openai.com/docs/api-reference/models/list
- https://api-docs.deepseek.com/api/list-models
- https://github.com/ollama/ollama-python
- https://github.com/openai/openai-python

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