Metatext MCP. Run predictions and manage datasets in natural conversation.

Metatext MCP Server: Your agent gets direct, granular access to every NLP model and dataset in your Metatext account. You treat your entire MLOps workflow like a conversation with your AI client. This server lets you manage models, run predictions, and build datasets without ever leaving the chat window.

Checking Model Inventory & Status

You can see everything you've trained. To get a complete list of every NLP model registered under your account, just ask for it; that triggers list_nlp_models. If you know what you’re looking for, you don’t have to wade through hundreds of results—you can narrow the search using search_nlp_models by matching keywords in a model's name or its capabilities tags.

When you pick one out, you can pull up its full profile using get_model_details. This gives you comprehensive data like versioning and current status flags for that specific NLP model.

Understanding which versions are live is key. To view every active or archived deployment instance of any model, trigger list_model_deployments. It shows you exactly what’s running right now versus what's sitting in cold storage. If you need to run a prediction—say, sentiment scoring or entity extraction—you send the input text directly to a specified model ID by invoking run_model_inference; it hands back the result immediately.

Managing Datasets and Data Points

The server gives you full control over your data pipelines. First, you can get an overview of everything available by asking for all datasets with list_nlp_datasets. Once you find a dataset ID that looks promising, you check its blueprint using get_dataset_details. This tool returns the complete schema and metadata, letting you know exactly what kind of data structure it expects.

If you need to see actual examples to audit the quality, you can run list_dataset_records, which lists multiple records within a dataset; remember, this function lets you control how many results are shown by using limits and offsets for proper pagination.

Need clean data for retraining? You use create_dataset_record to add single, new data points—or labels—to an existing dataset. This is how you build your training examples right from the agent conversation. These tools mean you don't have to jump between a dashboard and your chat interface; you just tell your AI client what needs fixing or updating.

Account Oversight and Usage Auditing

You can keep an eye on the health of the platform itself. To pull general usage metrics and check the overall account status for Metatext, you run get_account_info. This gives you a quick audit without having to log into billing or admin panels.

This server lets your AI agent handle everything from initial model discovery through live inference and data prep—all in one conversational flow. You tell it: 'Check the schema for Dataset X, then find Model Y, run it on this text, and record the output.' It runs the whole sequence automatically.