#Machine Learning MCP Servers

Run vector similarity searches on Oracle 23ai. Execute VECTOR_DISTANCE queries, inspect schemas, list vector indexes, and query tables from any AI agent.

Explore AI models, datasets and Spaces via Hugging Face. Search models, inspect files, review discussions and track collections from any AI agent.

Run ML models via Replicate. Generate images, text, audio and video from community models, track predictions and explore collections from any AI agent.

Access thousands of pre-trained AI models for NLP, vision, and audio tasks with the largest open-source machine learning hub.

Manage vector embeddings via Chroma. List collections, query embeddings, and audit document counts directly from any AI agent.

Equip your AI to dynamically search, run, and monitor thousands of open-source machine learning models hosted on Replicate via simple text commands.

Connect your AI to Supabase Vector. Execute pgvector semantic searches, manage embeddings, and run relational database queries directly from your terminal.

Generate code, evaluate embeddings, and deploy open-source LLMs instantly from your local agent via Together AI's infrastructure.

Track experiments, monitor ML runs, and manage artifacts on WandB. The developer platform for AI.

Manage AI inference via Clarifai. List apps, models, and workflows, and perform computer vision predictions directly from any AI agent.

Manage AI infrastructure via Lambda Labs. Launch GPU instances, monitor ML workloads, and manage SSH keys.

Manage AI-powered search via Lucidworks Fusion. Execute semantic queries, index documents, and monitor ML training jobs.

Extract structured data from any web page using AI that understands content like a human and builds knowledge graphs automatically.

Manage computer vision workflows. Upload images, train models, and manage datasets directly from your AI agent.

Automate high-quality data labeling and RLHF workflows via Scale AI. Create projects, manage batches, and submit annotation tasks directly from your AI agent.

Manage ML experiments via DVC. Track projects and views, audit experiments history, and monitor model runs directly from any AI agent.

Manage AI models via H2O.ai. Track data frames, monitor machine learning models and training jobs, and audit cloud cluster status directly from any AI agent.

Manage AI-powered data via MindsDB. Execute SQL predictions, audit ML models, and connect data sources.

Analyze and extract data from text via MonkeyLearn. Sentiment analysis and keyword extraction directly from your AI agent.

Provision and track powerful GPU workloads via Paperspace. List compute instances, fetch active deployments, trace team projects, and query Gradient environments via AI.

Analyze text data with custom machine learning models that classify sentiment, extract keywords, and tag topics automatically.

Manage machine learning-powered advertising via Moloco. Track campaigns and get analytics directly from your AI agent.

Classify data and perform semantic search via Nyckel. Track ML functions, samples, and labels directly from your AI agent.

Detect AI-generated text with confidence scores and highlight exactly which passages were likely written by a language model.

Standardize (Z-Score) or MinMax scale numeric columns with mathematical perfection local. Essential normalization for neural networks and clustering algorithms.

AI document extraction orchestration. Parse PDFs, images, and Word docs via AI.

No-code NLP and AI model management via Metatext. Run inference and manage datasets.

Deterministically convert categorical text columns into dummy binary variables local. Essential machine learning data prep without LLM data corruption.

Compute exact Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) for regression models. Stop hallucinating model validation metrics.

Power your agent with Vald. Query, insert, and manage dense vectors on a highly scalable, distributed nearest-neighbor engine.

Deterministically calculate True Positives, FP, Precision, Recall, F1-Score, and Accuracy local. Stop LLM hallucinations when evaluating model metrics.

Generate rigorous, leak-proof cross-validation indices for train and test splits in machine learning pipelines.