OneAI MCP. Turn messy media and documents into structured data.

You're running into data that's too big or too messy for a quick chat with your agent. This server handles complex media and texts by letting you expose specialized language skills through your AI client. It lets you run deep content analysis without needing to write custom models. You can use it if you're a data analyst or a dev who needs serious NLP power, fast.

Run synchronous analysis: When you need an answer right now, call run_pipeline. This executes instant Natural Language Processing (NLP) tasks directly within your conversation flow. For example, you can instantly summarize lengthy articles, detect shifts in sentiment, or pull out specific data points—structured entities like names, dates, and dollar amounts—from unstructured text input.

You just send the text, call run_pipeline, and get the result back immediately. No waiting.

Process large media files asynchronously: If you're dealing with something that takes time—think massive documents, hours of audio, or full video feeds—you don't want to wait for it to finish in real-time. That’s where run_async_pipeline comes into play. You submit the content using a URL or direct text input, and the system kicks off background processing.

It immediately gives you a task ID so you know the job is running, letting your agent move on while the heavy lifting happens in the background.

This workflow handles everything from transcribing spoken word to analyzing complex multimedia structures. You feed it audio or video content, and it converts that talking into accurate, plain text for further analysis. For documents too long for a single API call, you submit them here; this tool manages the whole lifecycle of processing those big files.

Check job completion status: Since run_async_pipeline just gives you a task ID and doesn't return the final data, you need another function to track it. You check progress using get_async_task_status. You poll this endpoint repeatedly until the job is marked as finished; then, you finally get the full result status and the processed content.

It’s how you manage stateful workflows for massive datasets.

You're not just wrapping up a few simple functions here. This server provides a complete pipeline: you submit big files with run_async_pipeline, track them with get_async_task_status, and when the data is ready, you've got access to deep skills for everything from extracting specific entities (like tracking every mention of a product name or a date range) to summarizing entire books.

It lets your agent handle both the immediate, quick checks and the long-haul processing jobs with total control.