# Clarifai (Vision AI) MCP for AI Agents AI Agent Connect > Clarifai (Vision AI) MCP lets you manage your computer vision projects and run model inferences directly through your AI agent. You can audit apps, browse models, check workflows, and get classification results from your Clarifai account without switching tabs. ## Overview - **Category:** artificial-intelligence - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_Wl7k3LILBATZT8oR2K1AY33RSlJewMKygOPyhcuJ/ai-agent-connect - **Tags:** computer-vision, machine-learning, model-inference, neural-networks, image-recognition, ai-workflows ## Description Connect your Clarifai account to your AI agent to handle your computer vision projects through a simple conversation. Instead of jumping between browser tabs to find specific models or check if a workflow is active, you can just ask your agent to do the work for you. You can trigger model predictions to see exactly how your neural networks evaluate images or get a clear look at your compute environments by listing your apps. This Connector helps you organize your machine learning tasks, whether you're auditing the concepts tagging your visual data or finding ways to isolate your logic. It makes it easy to verify outputs during the prototyping phase without writing extra boilerplate code. You'll find it much easier to manage complex AI logic across different execution contexts when these tools are available in your workspace. Finding this in the Vinkius catalog makes it simple to get your vision tools connected and ready to go. You get to focus on the high-level logic of your vision project while the agent handles the heavy lifting of searching through the Clarifai backend. You can quickly verify if your models are hitting limits or if your datasets are structured correctly without manually scouring through the dashboard. It turns a series of clicks into a single request, letting you stay in your flow while your agent handles the API calls and data parsing. ## Tools ### list_workflows See the structural matching for your chained AI limits. Use this to verify how your models are tied together in a sequence. ### list_apps See all your Clarifai apps and their global compute limits. This helps you keep your environment organized and audit your active compute. ### list_models Get the computer vision parameters driving your AI features. Use this to see the structural details of your active models. ### list_concepts See the semantic bounds and tags attached to your datasets. Use this to audit the textual concepts tagging your visual data. ### predict_model Run a validation inference and get the classification results for an image. It lets you see exactly what the neural network evaluated. ### list_datasets Identify the physical bounds and data structures for your visual nodes. This makes it easy to browse your training data. ## Prompt Examples **Prompt:** ``` What are the labels for my training data? ``` **Response:** ``` I've retrieved the concepts for your training data. Here are the semantic tags currently applied: | Concept | Tag Count | Status | | :--- | :--- | :--- | | **Urban Environment** | 1,240 | Active | | **Vehicle Detection** | 850 | Active | | **Pedestrian Flow** | 420 | Active | Would you like me to check the physical bounds for these datasets next? ``` **Prompt:** ``` Run a test on this image: https://example.com/test_photo.jpg ``` **Response:** ``` ### Inference Complete The model evaluated the image with the following results: * **Person**: 99.2% confidence * **Outdoors**: 95.4% confidence * **Mountain**: 92.1% confidence I have the full JSON response including bounding box coordinates if you need them for your report. ``` **Prompt:** ``` Which apps am I using? ``` **Response:** ``` You currently have 4 active Clarifai apps: * **Image-Moderation**: Standard limits applied. * **Face-Recognition**: High-priority compute. * **General-Vision**: Standard limits applied. * **Custom-Trainer**: Dedicated compute block. Let me know if you'd like to see the specific model parameters for any of these. ``` ## Capabilities ### Run image predictions Get classification results and bounding boxes from specific models instantly. ### Audit compute apps See all your Clarifai apps and their global compute limits in one list. ### Check workflow structures View the chained AI limits and blocks in your workflows to verify logic. ### Browse training data See the datasets and physical bounds for your visual nodes. ### Extract semantic tags View the concepts and labels attached to your datasets for auditing. ## Use Cases ### Verifying a new model A developer wants to see if a new model detects 'Person' correctly. They ask the agent to run a prediction on a test URL. ### Auditing data consistency A data scientist needs to see what concepts are tagging a specific dataset. They ask the agent to list the concepts for the 'training-v1' set. ### Managing compute limits An engineer needs to know which apps are hitting limits. They ask the agent to list all apps and their global limits. ### Debugging workflows A developer is confused about a chained logic block. They ask the agent to show the structural matching for their workflows. ## Benefits - Get faster results by using predict_model to run inferences without leaving your code editor. - Keep your environment organized by using list_apps to audit your compute limits. - Verify your logic by using list_workflows to see how your models are chained together. - Ensure data quality by using list_datasets and list_concepts to check your training tags. - Speed up prototyping by asking your agent to check model parameters using list_models. ## How It Works The bottom line is you can manage your whole computer vision stack through chat instead of clicking through a dashboard. 1. Connect your Clarifai account by providing your Personal Access Token. 2. Tell your AI agent which app or model you want to interact with. 3. Get back structured data like prediction results, app lists, or dataset details. ## Frequently Asked Questions **Can Clarifai (Vision AI) MCP run image classifications?** Yes, it allows your AI agent to run predictions on images and return classification results, including confidence scores and labels, directly in your chat. **How do I use Clarifai (Vision AI) MCP to check my models?** You can ask your agent to list the models in your account. It will provide the structural parameters and vision features driving your active AI tools. **Can I manage Clarifai workflows with this Connector?** Yes, you can use it to retrieve and verify the structural matching for your chained AI limits, making it easy to see how your models are sequenced. **Does Clarifai (Vision AI) MCP support dataset auditing?** It does. You can ask your agent to identify your datasets and extract the semantic concepts tagging your visual data to ensure training consistency. **How do I connect Clarifai (Vision AI) MCP to Claude?** Once you subscribe on Vinkius, you'll get the connection details to add to your Claude app. Just provide your Clarifai Personal Access Token to get started. **What can Clarifai (Vision AI) MCP do for ML engineers?** It allows engineers to monitor active compute brains, audit datasets, and verify model predictions using natural language instead of manual dashboard navigation. **Can my agent run image predictions using custom models?** Yes. Provide the User ID, App ID, and Model ID, along with the input JSON (containing image URLs or bytes). The agent calls Clarifai's predict API and returns exactly what the AI detected, from tags to bounding boxes. **How can I audit the datasets being used in my Clarifai app?** Ask your agent to list datasets for a specific app. It returns the precise physical bounds mapping the image sets, helping you ensure that your training loop is using the correct data boundaries. **Can I see all active workflows in my organization?** Absolutely. Use the 'list_workflows' tool. Your agent will pull the chained AI limits, showing you composed computational blocks that tie multiple neural networks together for complex visual tasks.