From 576d35a6e5277af52d150ee68923b7ca857c92a5 Mon Sep 17 00:00:00 2001 From: Dominik Safaric Date: Thu, 5 Feb 2026 22:13:12 +0100 Subject: [PATCH 1/4] Add MCP docs --- integrations/mcp.mdx | 111 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 111 insertions(+) create mode 100644 integrations/mcp.mdx diff --git a/integrations/mcp.mdx b/integrations/mcp.mdx new file mode 100644 index 0000000..5c7f250 --- /dev/null +++ b/integrations/mcp.mdx @@ -0,0 +1,111 @@ +--- +title: "MCP" +description: "" +--- + +Connect your AI tools to TabPFN using the [Model Context Protocol](https://modelcontextprotocol.io/) (MCP), +an open standard that lets AI assistants run our flagship [TabPFN-2.5](/models#tabpfn-2-5) model for instant predictions. + +## What is TabPFN MCP? + +TabPFN MCP is a remote MCP with OAuth that gives AI tools secure access TabPFN-2.5, our SOTA tabular foundation model. Our MCP server is available at: + +``` +https://api.priorlabs.ai/mcp/server +``` + +It integrates with popular AI assistants like Claude, enabling you to run predictions using natural language. TabPFN MCP implements the latest +[MCP Authorization](https://modelcontextprotocol.io/specification/2025-06-18/basic/authorization) and [Streamable HTTP](https://modelcontextprotocol.io/specification/2025-06-18/basic/transports#streamable-http) specifications. + +## Available tools + +The TabPFN MCP Server exposes two core tools for performing classification and regression on Prior Labs’ managed GPU infrastructure: + +* `fit_and_predict` - Fit and generate predictions in a single step using TabPFN-2.5. +* `predict` - Generate predictions using a previously fitted model. + +## Setup + +Connect your AI client to TabPFN MCP and authorize access to run inference through a natural language interface. + +#### Claude Code + +```bash +# If you haven't, install Claude Code +npm install -g @anthropic-ai/claude-code + +# Navigate to your project +cd your-tabpfn-project + +# Add TabPFN MCP (general access) +claude mcp add --transport http tabpfn https://api.priorlabs.ai/mcp/server + +# Start coding with Claude +claude + +# Authenticate the MCP tools by typing /mcp +# This will trigger the OAuth flow +/mcp +``` + +#### Claude.ai and Claude for desktop + +1. Open Settings in the sidebar +2. Navigate to Connectors and select Add custom connector +3. Configure the connector: + - Name: TabPFN + - URL: https://api.priorlabs.ai/mcp/server + + +Custom connectors using remote MCP are available on Claude and Claude Desktop for users on Pro, Max, Team, and Enterprise plans. + + +#### ChatGPT + +Follow these steps to set up TabPFN as a connector in ChatGPT: + +1. Enable Developer mode: + - Go to Settings → Connectors → Advanced settings → Developer mode +2. Open ChatGPT settings +3. In the Connectors tab, `Create` a new connector: + - Give it a name: TabPFN + - MCP server URL: https://api.priorlabs.ai/mcp/server + - Authentication: OAuth +4. Click Create + + +Custom connectors using MCP are available on ChatGPT for Pro and Plus accounts on the web. + + +#### Codex CLI + +Codex CLI is OpenAI's local coding agent that can run directly from your terminal. + +```bash +# Install Codex +npm i -g @openai/codex + +# Add TabPFN MCP +codex mcp add tabpfn --url https://api.priorlabs.ai/mcp/server + +# Start Codex +codex +``` + +When adding the MCP server, Codex will detect OAuth support and open your browser to authorize the connection. + +#### Cursor + +To add TabPFN MCP to your Cursor environment, add the snippet below to your project-specific or global `.cursor/mcp.json` file manually. For more details, see the [Cursor documentation](https://docs.cursor.com/en/context/mcp). + +```json +{ + "mcpServers": { + "tabpfn": { + "url": "https://api.priorlabs.ai/mcp/server" + } + } +} +``` + +Once the server is added, Cursor will attempt to connect and display a Needs login prompt. Click on this prompt to authorize Cursor to access your Prior Labs account. \ No newline at end of file From bdd0fa6802228b75f69806c63b155ecbead2db8c Mon Sep 17 00:00:00 2001 From: Dominik Safaric Date: Fri, 6 Feb 2026 13:12:11 +0100 Subject: [PATCH 2/4] Minor adjustments --- integrations/mcp.mdx | 32 +++++++++++++++++++++++++++++--- 1 file changed, 29 insertions(+), 3 deletions(-) diff --git a/integrations/mcp.mdx b/integrations/mcp.mdx index 5c7f250..56853ed 100644 --- a/integrations/mcp.mdx +++ b/integrations/mcp.mdx @@ -3,12 +3,11 @@ title: "MCP" description: "" --- -Connect your AI tools to TabPFN using the [Model Context Protocol](https://modelcontextprotocol.io/) (MCP), -an open standard that lets AI assistants run our flagship [TabPFN-2.5](/models#tabpfn-2-5) model for instant predictions. +Connect your AI tools to TabPFN using the [Model Context Protocol](https://modelcontextprotocol.io/) (MCP). ## What is TabPFN MCP? -TabPFN MCP is a remote MCP with OAuth that gives AI tools secure access TabPFN-2.5, our SOTA tabular foundation model. Our MCP server is available at: +TabPFN MCP is a remote MCP with OAuth that gives AI tools secure access to TabPFN-2.5, our SOTA tabular foundation model. Our MCP server is available at: ``` https://api.priorlabs.ai/mcp/server @@ -17,6 +16,10 @@ https://api.priorlabs.ai/mcp/server It integrates with popular AI assistants like Claude, enabling you to run predictions using natural language. TabPFN MCP implements the latest [MCP Authorization](https://modelcontextprotocol.io/specification/2025-06-18/basic/authorization) and [Streamable HTTP](https://modelcontextprotocol.io/specification/2025-06-18/basic/transports#streamable-http) specifications. + +To use the TabPFN MCP you need a Prior Labs account. You can sign up or log in at [ux.priorlabs.ai](https://ux.priorlabs.ai). + + ## Available tools The TabPFN MCP Server exposes two core tools for performing classification and regression on Prior Labs’ managed GPU infrastructure: @@ -60,6 +63,29 @@ claude Custom connectors using remote MCP are available on Claude and Claude Desktop for users on Pro, Max, Team, and Enterprise plans. +Alternatively, you may add the MCP server by editing the Claude Desktop config file: + +1. Locate your Claude Desktop config file based on your operating system: +2. Get your API key from Prior Labs: + - Navigate to [ux.priorlabs.ai](https://ux.priorlabs.ai) + - Log in to your account (or sign up if you don't have one) + - Copy your API key from the dashboard +3. Edit the config file to add the TabPFN server: +```json +{ + "mcpServers": { + "tabpfn": { + "url": "https://api.priorlabs.ai/mcp/server", + "headers": { + "Authorization": "Bearer YOUR_API_KEY_HERE" + } + } + } + } +``` +4. Replace `YOUR_API_KEY_HERE` with your actual API key from step 2 +5. Save the config file and restart Claude Desktop for the changes to take effect + #### ChatGPT Follow these steps to set up TabPFN as a connector in ChatGPT: From acb0c9ae8a48ba0a609242fb99753af1854f6676 Mon Sep 17 00:00:00 2001 From: Dominik Safaric Date: Fri, 6 Feb 2026 13:29:44 +0100 Subject: [PATCH 3/4] Add tool schema --- integrations/mcp.mdx | 164 ++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 163 insertions(+), 1 deletion(-) diff --git a/integrations/mcp.mdx b/integrations/mcp.mdx index 56853ed..d852a00 100644 --- a/integrations/mcp.mdx +++ b/integrations/mcp.mdx @@ -134,4 +134,166 @@ To add TabPFN MCP to your Cursor environment, add the snippet below to your proj } ``` -Once the server is added, Cursor will attempt to connect and display a Needs login prompt. Click on this prompt to authorize Cursor to access your Prior Labs account. \ No newline at end of file +Once the server is added, Cursor will attempt to connect and display a Needs login prompt. Click on this prompt to authorize Cursor to access your Prior Labs account. + +## Tool Reference + +### `fit_and_predict` + + +Fit the TabPFN-2.5 model on your data and generate predictions. + + +Use this tool when you want to fit a new model from scratch. It fits on your data and immediately returns predictions for your test set, along with a `model_id` for future reuse. + +#### Required Parameters + + + Training features as a 2D array where rows represent samples and columns represent features. + + - **Shape:** `(n_train_samples, n_features)` + - **Data types:** Numeric (int/float) or categorical (string) values + - **Flexibility:** Handles missing values, outliers, and mixed data types automatically + + + ```python + X_train = [ + [1.5, "red", 3], + [2.0, "blue", 4], + [1.8, "red", 5] + ] + ``` + + + + + Training targets as a 1D array that must align with `X_train` rows. + + - **Shape:** `(n_train_samples,)` + - **Classification:** Class labels (e.g., `[0, 1, 0]` or `["cat", "dog", "cat"]`) + - **Regression:** Numeric values (e.g., `[23.5, 45.2, 12.8]`) + + + + Test features as a 2D array for generating predictions. + + - **Shape:** `(n_test_samples, n_features)` + - **Critical:** Must have the **same number of features** as `X_train` + + + ```python + X_test = [ + [1.8, "red", 5], + [2.3, "green", 2] + ] + ``` + + + + + Prediction task type. Must be specified explicitly. + + - `"classification"` - For classification tasks. + - `"regression"` - For regression tasks. + + +#### Optional Parameters + + + Format of the prediction output. + + - `"preds"` - Predicted class labels (classification) or mean values (regression) + - `"probas"` - Class probability distributions (**classification only**) + + + `output_type="probas"` is only valid for classification tasks and will return an error for regression. + + + + ```python + # For a binary classification problem + predictions = [ + [0.8, 0.2], # 80% probability class 0, 20% class 1 + [0.3, 0.7] # 30% probability class 0, 70% class 1 + ] + ``` + + + +#### Returns + + + Unique identifier for the fitted model. **Save this value** to reuse the model later with the `predict` tool. + + + + Prediction results based on the specified `output_type`. Format varies by task type and output type. + + +--- + +### `predict` + + +Generate new predictions using a previously fitted TabPFN model. + + +Use this tool after calling `fit_and_predict` to make predictions on new data using an existing model. + +#### Required Parameters + + + Identifier of a previously fitted model, returned from `fit_and_predict`. + + + ```python + model_id = "9f1526b2-388b-4849-b965-6373d35f1a6b" + ``` + + + + + Test features as a 2D array for generating predictions. + + - **Shape:** `(n_test_samples, n_features)` + - **Critical:** Must have the **same number of features** the model was originally fitted on + + + ```python + X_test = [ + [1.2, "red", 7], + [3.4, "blue", 1] + ] + ``` + + + + + Must match the task type the model was fitted for. + + - `"classification"` - For classification tasks + - `"regression"` - For regression tasks + + +#### Optional Parameters + + + Format of the prediction output. + + - `"preds"` - Predicted labels (classification) or values (regression) + - `"probas"` - Probability distributions (**classification models only**) + + + `output_type="probas"` only works with classification models. + + + +#### Returns + + + Echo of the model ID used for prediction. + + + + Prediction results based on the specified `output_type`. + From aa5736a38da176edd65f67e1a668504bdfd3edf4 Mon Sep 17 00:00:00 2001 From: Dominik Safaric Date: Fri, 6 Feb 2026 13:34:38 +0100 Subject: [PATCH 4/4] Added youtuben --- integrations/mcp.mdx | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/integrations/mcp.mdx b/integrations/mcp.mdx index d852a00..51eed5e 100644 --- a/integrations/mcp.mdx +++ b/integrations/mcp.mdx @@ -53,6 +53,14 @@ claude #### Claude.ai and Claude for desktop + + 1. Open Settings in the sidebar 2. Navigate to Connectors and select Add custom connector 3. Configure the connector: @@ -88,6 +96,14 @@ Alternatively, you may add the MCP server by editing the Claude Desktop config f #### ChatGPT + + Follow these steps to set up TabPFN as a connector in ChatGPT: 1. Enable Developer mode: @@ -136,6 +152,18 @@ To add TabPFN MCP to your Cursor environment, add the snippet below to your proj Once the server is added, Cursor will attempt to connect and display a Needs login prompt. Click on this prompt to authorize Cursor to access your Prior Labs account. +#### n8n + +Watch the video below to learn how to integrate TabPFN with n8n workflows. + + + ## Tool Reference ### `fit_and_predict`