SenseCore Platform MCP. Control model runs, compute resources, and completions.

SenseCore Platform gives your AI agent the muscle it needs to run serious industrial-grade model workflows through MCP. You're not just connecting to an API; you're getting full control over the entire lifecycle, from defining a new assistant to tracking complex background jobs. Here’s what you can do with these tools.

Orchestrating Model Conversations

When your agent needs to talk, it uses chat_completions to send messages through SenseCore's large language model engine. You pass in a message and the specific SenseTime model you want to use; the engine handles the rest. If you need to maintain context—which you always do—you first initialize a brand new conversation using create_thread.

That gives your agent an empty history it can build on. Then, before running anything complex, you'll use list_messages to pull the entire message history for that thread ID so your agent remembers everything that was said. To add content at any point, even if you just want to inject a document reference or correct a previous turn, you call create_message, appending it directly to that existing conversation context.

When everything is set up—the thread exists, the messages are there, and the model is ready—you kick off the core action with create_run. This tool tells your agent to execute the defined assistant against the current message thread, initiating the full workflow run.

Managing AI Assistant Lifecycles

You don't just want a random chat; you need a structured process. That’s where dedicated assistants come in. You can set up and define a completely new, specialized AI assistant using create_assistant. This defines the rules for multi-step or guided conversations within your project. Once it's defined, if you need to know what kind of assistant is already running around, you run list_assistants to get a full roster of everything configured in your account.

If you want deep technical specs on a specific helper—like how many steps it has or which model it defaults to—you use get_assistant_details and pass in its ID. These tools let you build, manage, and audit the intelligence layer before any messages are even sent.

Discovering Model Specs and Files

If you don't know exactly what models you have access to, you run list_models. This gives you a complete catalog of every available SenseNova foundation model and tracks their specific versions. It’s your source of truth for what engines are running. Furthermore, if your workflow involves data—whether it’s reference documents or user uploads—you can use list_files to pull a list of all files uploaded to the current workspace or thread context, ensuring your agent knows exactly what data it's working with before it processes anything.

Monitoring and Tracking Jobs

Running an LLM isn't always instant. Sometimes you're talking about huge batches of inference or long training runs that take forever. You don't want to stare at a loading bar all day, so you use get_run_status. This checks the current status—whether it's running, if it failed, or if it completed successfully—for any complex assistant run you started earlier.

It keeps you in the loop without having to manually poll for updates.

In short, you get a complete operational view: you can define your workflow with dedicated assistants, pull the specific models and data files needed, start the conversation thread, kick off the job, and then constantly monitor its status until it's done. It’s comprehensive control over everything running on SenseCore.