Integrations
Enables access to Google Gemini models including Gemini 2.5 Pro, allowing prompt processing through a standardized interface.
Allows interaction with locally-hosted Ollama models through a consistent API, supporting models like Llama 3.1.
Provides access to OpenAI's models including GPT-4o and GPT-4o-mini through a unified interface for prompt processing.
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. See how we use the ceo_and_board
tool to make hard decisions easy with o3 here.
Tools
The following MCP tools are available in the server:
prompt
: Send a prompt to multiple LLM models- Parameters:
text
: The prompt textmodels_prefixed_by_provider
(optional): List of models with provider prefixes. If not provided, uses default models.
- Parameters:
prompt_from_file
: Send a prompt from a file to multiple LLM models- Parameters:
file
: Path to the file containing the promptmodels_prefixed_by_provider
(optional): List of models with provider prefixes. If not provided, uses default models.
- Parameters:
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 promptmodels_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
- Parameters:
ceo_and_board
: Send a prompt to multiple 'board member' models and have a 'CEO' model make a decision based on their responses- Parameters:
file
: Path to the file containing the promptmodels_prefixed_by_provider
(optional): List of models with provider prefixes to act as board members. If not provided, uses default models.output_dir
(default: "."): Directory to save the response files and CEO decisionceo_model
(default: "openai:o3"): Model to use for the CEO decision in format "provider:model"
- Parameters:
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')
- Parameters:
Provider Prefixes
every model must be prefixed with the provider name
use the short name for faster referencing
o
oropenai
: OpenAIo:gpt-4o-mini
openai:gpt-4o-mini
a
oranthropic
: Anthropica:claude-3-5-haiku
anthropic:claude-3-5-haiku
g
orgemini
: Google Geminig:gemini-2.5-pro-exp-03-25
gemini:gemini:gemini-2.5-pro-exp-03-25
q
orgroq
: Groqq:llama-3.1-70b-versatile
groq:llama-3.1-70b-versatile
d
ordeepseek
: DeepSeekd:deepseek-coder
deepseek:deepseek-coder
l
orollama
: Ollamal: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
Environment Variables
Create a .env
file with your API keys (you can copy the .env.sample
file):
Then edit the .env
file to add your API keys (or export them in your shell):
Claude Code Installation
In all these examples, replace the directory with the path to the just-prompt directory.
Default models set to openai:o3:high
, openai:o4-mini:high
, anthropic:claude-3-7-sonnet-20250219:4k
, gemini:gemini-2.5-pro-preview-03-25
, and gemini:gemini-2.5-flash-preview-04-17
.
If you use Claude Code right out of the repository you can see in the .mcp.json file we set the default models to...
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
JSON to copy
With a custom default model set to openai:gpt-4o
.
With multiple default models:
Using mcp add
with project scope
mcp remove
claude mcp remove just-prompt
Running Tests
Codebase Structure
Context Priming
READ README.md, pyproject.toml, then run git ls-files, and 'eza --git-ignore --tree' to understand the context of the project.
Reasoning Effort with OpenAI o‑Series
For OpenAI o‑series reasoning models (o4-mini
, o3-mini
, o3
) you can
control how much internal reasoning the model performs before producing a
visible answer.
Append one of the following suffixes to the model name (after the provider prefix):
:low
– minimal internal reasoning (faster, cheaper):medium
– balanced (default if omitted):high
– thorough reasoning (slower, more tokens)
Examples:
openai:o4-mini:low
o:o4-mini:high
When a reasoning suffix is present, just‑prompt automatically switches to
the OpenAI Responses API (when available) and sets the corresponding
reasoning.effort
parameter. 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.
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 tokensanthropic:claude-3-7-sonnet-20250219:4k
- Use 4096 thinking tokensanthropic:claude-3-7-sonnet-20250219:8000
- Use 8000 thinking tokens
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.)
Thinking Budget with Gemini
The Google Gemini model gemini-2.5-flash-preview-04-17
supports 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 budgetgemini:gemini-2.5-flash-preview-04-17:4k
- Use 4096 thinking budgetgemini:gemini-2.5-flash-preview-04-17:8000
- Use 8000 thinking budget
Notes:
- Thinking budget is only supported for the
gemini-2.5-flash-preview-04-17
model - 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.)
Resources
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hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
A lightweight MCP server that provides a unified interface to various LLM providers including OpenAI, Anthropic, Google Gemini, Groq, DeepSeek, and Ollama.
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