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Use the LLM playground to test your server against different LLMs. This is helpful to simulate how LLMs interpret your server and test for hallucinations. The playground is also great for simulating agent behaviors. You can customize the system prompt, swap models, and configure system prompts and temperature as if you would building an agent.
MCPJam Inspector Logo

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Set up LLM Playground

You need to set up at least one LLM to use the playground. Go to the settings tab in the inspector and follow instructions from there.

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OpenAI

Get an API key from OpenAI Platform. gpt-4o, gpt-4o-mini, gpt-4-turbo, gpt-4, gpt-5.1, gpt-5.1-codex, gpt-5.1-codex-mini, gpt-5, gpt-5-mini, gpt-5-nano, gpt-5-chat-latest, gpt-5-pro, gpt-5-codex, gpt-4.1, gpt-4.1-mini, gpt-4.1-nano, gpt-3.5-turbo,o3-mini, o3, o4-mini, o1
GPT-5 models require organization verification. If you encounter access errors, visit OpenAI Settings and verify your organization. Access may take up to 15 minutes after verification.
GPT-5 models do not support temperature configuration. The temperature setting will be automatically disabled when using GPT-5 models.

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Claude (Anthropic)

Get an API key from Anthropic Console. claude-opus-4-1, claude-opus-4-0, claude-sonnet-4-5, claude-sonnet-4-0, claude-3-7-sonnet-latest, claude-haiku-4-5, claude-3-5-haiku-latest

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Gemini

Get an API key from Google AI Studio gemini-3-pro-preview, gemini-2.5-pro, gemini-2.5-flash, gemini-2.5-flash-lite, gemini-2.0-flash-exp, gemini-1.5-pro, gemini-1.5-pro-002, gemini-1.5-flash, gemini-1.5-flash-002, gemini-1.5-flash-8b, gemini-1.5-flash-8b-001, gemma-3-2b, gemma-3-9b, gemma-3-27b, gemma-2-2b, gemma-2-9b, gemma-2-27b, codegemma-2b, codegemma-7b

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Deepseek

Get an API key from Deepseek Platform deepseek-chat, deepseek-reasoner

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Mistral AI

Get an API key from Mistral AI Console mistral-large-latest, mistral-small-latest, codestral-latest, ministral-8b-latest, ministral-3b-latest

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OpenRouter

Get an API key from OpenRouter Console Select from any tool-capable model in the dropdown.

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Ollama

Make sure you have Ollama installed, and the MCPJam Ollama URL configuration is pointing to your Ollama instance. Start an Ollama instance with ollama serve <model>. MCPJam will automatically detect any Ollama models running.

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LiteLLM Proxy

Use LiteLLM Proxy to connect to 100+ LLMs through a unified OpenAI-compatible interface.
  1. Start LiteLLM Proxy: Follow the LiteLLM Proxy Quick Start Guide to set up your proxy server
  2. Configure in MCPJam: Go to Settings → LiteLLM card → Click “Configure”
  3. Enter Connection Details:
    • Base URL: Your LiteLLM proxy URL (default: http://localhost:4000)
    • API Key: Your proxy API key (use the same key you use in your API requests)
    • Model Aliases: Comma-separated list of model names configured in your proxy (e.g., gpt-3.5-turbo, claude-3-opus, gemini-pro)
Use the exact model names that work with your LiteLLM proxy’s /v1/chat/completions endpoint. These are typically the model names without provider prefixes (e.g., gpt-3.5-turbo instead of openai/gpt-3.5-turbo).
Example configuration: 
# In your LiteLLM config.yaml
model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: openai/gpt-3.5-turbo
      api_key: os.environ/OPENAI_API_KEY
Then in MCPJam, use gpt-3.5-turbo as the model alias.

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Choose an LLM model

Once you’ve configured your LLM API keys, go to the Playground tab. On the bottom near the text input, you should see a LLM model selector. Select the model from the ones you’ve configured
Model selector

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System prompt and temperature

You can configure the system prompt and temperature, just like you would building an agent. The temperature is defaulted to the default value of the LLM providers (Claude = 0, OpenAI = 1.0).
Higher temperature settings tend to hallucinate more with MCP interactions
Model selector

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Using MCP prompts in chat

You can use MCP prompts directly in the playground chat by typing / to trigger the prompts menu. When you select a prompt, it appears as an expandable card above the chat input showing:
  • Server name - Which MCP server provides the prompt
  • Description - What the prompt does
  • Arguments - Required and optional parameters
  • Preview - A preview of the prompt content
Click the card to expand/collapse details, or click the X button to remove it before sending. This helps you understand what will be sent to the LLM before submitting your message.

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Playground layout

The playground features a split-panel layout with:
  • Chat panel (left) - Your conversation with the LLM, including tool calls and results
  • JSON-RPC logger (right) - Real-time view of MCP protocol messages between Inspector and your servers
You can resize the panels by dragging the divider between them. This layout helps you understand exactly how the LLM interacts with your MCP servers.

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Error handling

When errors occur during playground interactions, you’ll see an error message with a “Reset chat” button. For detailed debugging, click “More details” to expand additional error information, including JSON-formatted error responses when available. This helps you quickly identify and resolve issues with your MCP server or LLM configuration.

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Elicitation support

MCPJam has elicitation support in the LLM playground. Any elicitation requests will be shown as a popup modal.

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MCP-UI support

The playground supports rendering custom UI components from MCP servers using the MCP-UI specification. When an MCP tool returns a UI resource, it will be rendered inline in the chat with interactive capabilities. MCP-UI components can:
  • Display rich, interactive visualizations
  • Trigger tool calls through button actions
  • Send follow-up messages to the chat
  • Open external links
  • Show notifications
This enables MCP server developers to create custom user experiences beyond plain text responses.

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OpenAI Apps SDK support

The playground supports rendering custom UI components from MCP tools using the OpenAI Apps SDK. When a tool includes an openai/outputTemplate metadata field pointing to a resource URI, the playground will render the custom HTML interface in an isolated iframe with access to the window.openai API. This enables MCP servers to provide rich, interactive visualizations for tool results, including charts, forms, and custom widgets that can call other tools or send followup messages to the chat.

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Display modes

OpenAI Apps can request different display modes to optimize their presentation:
  • Inline (default) - Widget renders within the chat message flow
  • Picture-in-Picture - Widget floats at the top of the screen, staying visible while you scroll through the chat
  • Fullscreen - Widget expands to fill the entire viewport for immersive experiences
Widgets can request display mode changes using window.openai.requestDisplayMode({ mode: 'pip' }) or window.openai.requestDisplayMode({ mode: 'fullscreen' }). Users can exit PiP or fullscreen modes by clicking the close button in the top-left corner of the widget.