Run predictions and manage datasets in natural conversation. 
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Metatext MCP Server gives your AI agent direct access to advanced NLP model management. Use this server to list all trained models (`list_nlp_models`), check dataset metadata, run real-time predictions via `run_model_inference`, and manage data pipelines by creating records or fetching account info.
It lets you treat your entire MLOps workflow like a conversation.
What your AI can do
Create dataset record
Adds a single new record (data point) to an existing dataset.
Get account info
Retrieves general usage metrics and account status information for the Metatext platform.
Get dataset details
Fetches the complete structure, schema, and metadata for a specified dataset ID.
List every trained NLP model available in the account using list_nlp_models or quickly search for a specific one with search_nlp_models.
Execute real-time predictions on deployed models by passing input text to run_model_inference.
Enumerate all datasets (list_nlp_datasets), check dataset structure with get_dataset_details, or add new data points using create_dataset_record.
Retrieve detailed metadata for specific models (get_model_details) and list active deployment instances via list_model_deployments.
Get usage metrics and account health information using the get_account_info tool.
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Compatible AI Apps
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Metatext MCP Server: 10 Tools for Model & Data Ops
These tools let your AI agent interact directly with your Metatext account to list models, run predictions, and manage data pipelines.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Metatext on VinkiusCreate Dataset Record
Adds a single new record (data point) to an existing dataset.
Get Account Info
Retrieves general usage metrics and account status information for the Metatext...
Get Dataset Details
Fetches the complete structure, schema, and metadata for a specified dataset ID.
Get Model Details
Retrieves comprehensive details (versioning, status) for a specific NLP model by its...
List Nlp Datasets
Returns a complete list of every dataset available in the Metatext account, often...
List Model Deployments
Shows all active and archived deployments of NLP models, helping track which version is live.
List Nlp Models
Queries and returns a comprehensive list of all trained NLP models registered under your account.
List Dataset Records
Lists multiple data records within a dataset, allowing you to paginate results based...
Run Model Inference
Sends text input to a specified model ID to receive a prediction, classification, or...
Search Nlp Models
Finds specific NLP models by matching keywords in their name or capabilities tags.
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Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The Metatext integration is available immediately — no restart needed.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Metatext, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,000+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Metatext. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 10 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The workflow always breaks when you have to copy data between tools.
Right now, if you want to test a model's ability on new text, the process is a mess. You pull your dataset from one dashboard, export it to a CSV, load that CSV into another service just for inference testing, and then manually write down the results in a third document. It’s slow, prone to version mismatch, and requires moving data out of its native context.
With Metatext MCP Server, you keep everything inside your agent's memory. You ask it to run `list_nlp_datasets`, confirm details with `get_dataset_details`, provide the text, and tell it to run the prediction using `run_model_inference`. The results come back instantly, structured, and ready for use.
Metatext MCP Server: You manage models by conversation.
The biggest time sink in MLOps is the 'discovery' phase. You spend hours trying to remember which model ID handles sentiment vs. named entity recognition, and you have to check multiple dashboards just to see if it’s even deployed. This means wasted cycles and stalled development.
This server solves that with `search_nlp_models` and `list_nlp_models`. You ask your agent, 'What models handle customer complaints?' It gives you the names and IDs immediately. No manual searching, no guessing—just actionable data.
What your AI can actually do with this
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.
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Here's how it actually works
The bottom line is: you tell your AI client what you need in plain English, and it uses these tools to run the complex ML operations for you.
Connect your AI agent to the Metatext MCP Server and provide your API key.
The agent analyzes your request, determining which tools are needed (e.g., list_nlp_models followed by run_model_inference).
The server executes the sequence of tool calls, returns structured data (like model lists or prediction scores), and relays it back to the agent for a final conversational answer.
Who is this actually for?
Data Scientists who are tired of context-switching between dashboard UIs and code environments. It’s for the MLOps engineer who needs to audit model performance, test new datasets, or run ad-hoc predictions without writing boilerplate API calls every time. If your job involves moving data from 'idea' to 'prediction,' this is for you.
Manages the model lifecycle: running list_nlp_models to check versions, calling run_model_inference for validation tests, and updating metadata via get_model_details.
Prepares datasets by listing available data with list_nlp_datasets, inspecting records using list_dataset_records, and adding new labeled examples with create_dataset_record.
Monitors the system: checking deployment status via list_model_deployments or auditing account limits using get_account_info.
What Changes When You Connect
Execute Inference on Demand: Instead of writing a script just to test a model, you simply ask your agent to run run_model_inference with the text. You get immediate classification scores or extractions without boilerplate code.
Full Data Lifecycle Control: The server lets you manage data from start to finish. You can use list_nlp_datasets to see what exists, then create_dataset_record to add a new labeled example for training, and finally monitor the process with get_model_details.
Zero-Friction Model Discovery: Need to know which model handles entity extraction? Use search_nlp_models. It finds models by name or capability tag in one step, skipping manual browsing of dozens of dashboards.
Auditability Built-In: You don't just run models; you audit them. Use list_model_deployments to see exactly which model version is running live and what the historical usage patterns are via get_account_info.
Conversational Data Prep: Forget complex YAML files for basic data tasks. The agent can talk to you: 'List my datasets' (list_nlp_datasets), then 'Show me details on that one' (get_dataset_details).
Direct API Access via Chat: Everything normally requiring 5-10 distinct API endpoints—listing, getting details, running inference—is wrapped into a single, conversational tool call.
See it in action
Validating Model Drift
A data scientist suspects their 'Sentiment Classifier' is performing worse. They prompt the agent: "List my models and check the details for Sentiment Classifier." The agent uses list_nlp_models and then get_model_details. Next, they provide a batch of new, unclassified customer reviews, asking the agent to run them through run_model_inference to compare against old metrics. This quickly identifies if model drift is occurring.
Building Training Data Sets
A team needs more examples of 'Refund requested' support tickets for a new model. They use the agent to run list_nlp_datasets to find the correct dataset ID, then ask it to check the schema via get_dataset_details. Finally, they pass them 20 raw text snippets and instruct the agent to use create_dataset_record for each one, labeling it 'Support' in a single flow.
Auditing Production Deployments
The platform engineer needs to know if Model A or Model B is currently live and which version was used last week. They call list_model_deployments to see all active endpoints, then use get_account_info to check the overall usage against billing thresholds before approving a new deployment.
Quick Model Lookup
A developer is working on a client-facing feature and needs an entity extractor but doesn't know the exact model ID. They ask the agent to 'Show me all models that extract names.' The agent immediately uses search_nlp_models by capability, providing the required model ID without any manual searching.
The honest tradeoffs
Treating it like a simple database query
Prompting: "Give me all models and their data." The agent gets confused because 'models' are code, not records, and 'data' is too vague.
Break the request into steps. First, use list_nlp_models to see what exists. Then, if you need a model's details, run get_model_details. If you need data, use list_nlp_datasets first.
Assuming all models are ready for inference
Calling run_model_inference immediately after listing models. This fails if the model is listed but not actively deployed.
Always check deployment status first. Use list_model_deployments to confirm an active version exists before attempting to run prediction via run_model_inference.
Trying to batch-process everything in one prompt
Prompting: "List datasets, create 5 records, and then run inference on all of them." This is too complex for a single tool call.
Use the agent's conversational flow. First, ask list_nlp_datasets. Second, confirm details with get_dataset_details. Third, execute data actions one by one using specific tools like create_dataset_record.
When It Fits, When It Doesn't
Use this server if your workflow requires a multi-step process involving NLP model management (MLOps). Specifically, you need to move beyond simple CRUD operations and involve actual computation—that's when you need run_model_inference. This is essential for data scientists validating models or engineers building pipelines.
Don't use this if your primary goal is just storing text documents; that’s a document storage service. Also, don't rely on it for general API monitoring; while get_account_info helps, dedicated billing tools are better for finance. If you only need to list things and nothing else, the tool set works, but its true value comes from chaining calls: Listing models -> Getting details -> Running inference.
Questions you might have
How do I list all my NLP datasets using Metatext MCP Server? +
You call list_nlp_datasets. This tool returns a comprehensive inventory of every dataset ID in your account, allowing you to see what's ready for work.
What is the difference between `get_dataset_details` and `list_dataset_records`? +
get_dataset_details shows the schema—the blueprint of the dataset. list_dataset_records actually retrieves the data points inside it, letting you inspect the content.
Can I run predictions on a model that isn't deployed? +
No. You must first use list_model_deployments to verify an active deployment exists before calling run_model_inference. The server won't let you run it otherwise.
How do I add new training data using Metatext MCP Server? +
You use the create_dataset_record tool. You must provide the specific dataset ID and all necessary fields to ensure the record is added correctly.
What steps do I take to ensure my agent can connect using `get_account_info`? +
You must provide a valid Metatext API Key. This key authenticates your client and grants the agent access to specific account metrics, such as usage limits or overall resource consumption.
How does `list_model_deployments` show me which NLP models are ready for use? +
It lists all currently active model endpoints. This tool doesn't just list models; it confirms their deployment status, telling your agent exactly where to send real-time inference requests.
If I only know the name of a model, how do I use `search_nlp_models`? +
It filters your entire catalog by partial or full names. Instead of reviewing every trained NLP model, this tool quickly surfaces specific models you need for immediate inspection or testing.
When using `list_dataset_records`, what happens if my dataset is huge? +
The tool retrieves data in paginated batches. You'll need to check the response metadata for pagination tokens or a next page URL to pull every available record, preventing API rate limits.
How do I find my Metatext API Key? +
Log in to Metatext and navigate to your account settings to find and copy your API Key.
Can I run inference on any model type? +
Yes, as long as the model is fully trained and deployed, you can use the run_model_inference tool.
Is my AI data secure? +
Absolutely. Your token is encrypted at rest and injected securely at runtime.
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