# Anyscale MCP MCP

> Anyscale MCP connects your AI agent directly to complex, distributed ML infrastructure. You can list available models, run generative queries, create semantic vector embeddings, and check the status of massive batch jobs without opening a terminal or cloud dashboard. It’s control over your entire LLM lifecycle from one conversation.

## Overview
- **Category:** ai-frontier
- **Price:** Free
- **Tags:** distributed-computing, llm-inference, vector-embeddings, cluster-management, scalable-ai

## Description

You shouldn't have to jump between a web console, a command line, and an AI chat interface just to run a single task. This MCP lets you manage the whole stack—from model discovery to job completion—all through natural conversation with your agent. Need to know what LLMs are available? You ask, and it lists them for you. Got text data that needs context? Pass it in, and it generates vectors on the fly. If a training run stalled out or an endpoint isn't responding, you just ask for the job status or service details. It pulls all that deep infrastructure info into your chat window immediately. This makes debugging deployments way faster. When you connect this Anyscale MCP through Vinkius, your agent knows exactly how to call these tools, so you’re not stuck in any single UI flow.

## Tools

### chat_completion
Generates conversational responses using foundational LLMs for chat-style queries.

### generate_embeddings
Creates semantic vector embeddings from text inputs for context retrieval.

### get_service
Retrieves specific configuration and operational details about a single Anyscale service.

### list_jobs
Lists all historical or running batch and training jobs on the cluster, including their status.

### list_models
Retrieves a list of foundational AI models currently available for inference.

### list_services
Provides a complete directory listing of all deployed Anyscale services.

### text_completion
Generates raw text completions using a generic foundational instruction API.

## Prompt Examples

**Prompt:** 
```
List all active models from my Anyscale cluster.
```

**Response:** 
```
I've fetched your models. Currently, you have `meta-llama/Llama-2-70b-chat-hf` and `mistralai/Mistral-7B-Instruct-v0.1` ready to receive inference traffic.
```

**Prompt:** 
```
Check the status of our latest training jobs.
```

**Response:** 
```
Looking up recent jobs. The 'daily_retrain_v3' job succeeded 5 hours ago. However, 'experimental_tuning_7b' was interrupted due to out-of-memory errors on node 12. Would you like me to fetch more service metadata on that specific node?
```

**Prompt:** 
```
Generate vector embeddings for the text: 'The sun rises in the east and sets in the west'
```

**Response:** 
```
I've passed the text to your configured Anyscale embedding model. The embedding array generated successfully with 768 dimensions. Since the array is large, I've mapped the output directly to the data pipeline.
```

## Capabilities

### Discover and query foundational models
List all active LLMs running on the cluster or run conversational prompts against them.

### Generate text embeddings from data
Convert arrays of raw text into semantic vector embeddings for immediate use in retrieval systems.

### Check service deployment status
Retrieve detailed metadata and current operational state for specific deployed microservices.

### Monitor batch job execution history
Get the last known status, metrics, or failure reasons for any running Ray cluster jobs.

### List all available services
Fetch an enumeration of every currently deployed service within the Anyscale environment.

## Use Cases

### Debugging a failing endpoint
A developer notices service A is returning 503 errors. Instead of logging into the cloud console, they ask their agent to run `get_service` on 'Service A'. The agent returns the exact metadata and current cluster state in seconds.

### Validating a retraining pipeline
The MLOps engineer needs to confirm if yesterday's model update actually ran. They ask their agent to run `list_jobs`. The system replies, showing the 'retrain_v4' job succeeded and listing its final metrics.

### Building an RAG prototype
A data scientist has a large PDF corpus. They ask their agent to process the text chunk by chunk using `generate_embeddings`, sending the resulting vector array directly into the memory for immediate query use.

### Checking available LLM options
Before writing any code, a developer needs to know if Mistral or Llama 2 is deployed. They ask their agent to run `list_models` and get the full list of available chat models in one go.

## Benefits

- Stop digging through dashboards. You can check the status of complex batch jobs and training metrics instantly by calling `list_jobs` directly from your agent.
- Context switching ends when you need vectors. Instead of exporting text and running a separate script, simply use `generate_embeddings` to process data in-flight.
- Need to know what's running? You get a full inventory using `list_services`, which provides an immediate map of every deployed endpoint.
- Model discovery is simple. Use `list_models` to see exactly which foundational LLMs are ready for your next query, no guesswork required.
- The agent can handle both quick chat queries via `chat_completion` and detailed technical lookups using `get_service`—all without changing tools.

## How It Works

The bottom line is: it lets your AI client talk directly to your MLOps backend without you touching a dashboard.

1. First, you subscribe to this MCP and provide your Anyscale API key and base URL.
2. Next, you direct your agent to perform a task—like checking job status or generating embeddings—via natural language prompts.
3. Finally, the MCP executes the appropriate internal tool call and returns structured data directly into your conversation.

## Frequently Asked Questions

**How do I check if my LLMs are deployed using list_models?**
You run `list_models` directly with your agent. It returns a clean list of all available models, like Llama-2 or Mistral, so you know exactly what's ready for inference.

**What is the difference between list_services and get_service?**
`list_services` gives you a directory of everything deployed. Use `get_service` when you need deep, specific details on one particular service to debug its state.

**Can I use generate_embeddings for chat_completion tasks?**
No. `generate_embeddings` creates numerical vector data, which is used for retrieval or context search. For conversational replies, you must use the `chat_completion` tool.

**Does list_jobs show me when a job failed?**
Yes, absolutely. When you run `list_jobs`, it shows the execution status and failure reasons for batch or training jobs, helping you pinpoint what broke.

**When using `chat_completion`, what credentials must I provide to connect my agent?**
You need your Anyscale API Key and Base URL, which you pass during the MCP setup. This connection data allows your AI client to authenticate all requests before running any model functions.

**If I send a massive array of texts using `generate_embeddings`, how does it handle rate limits?**
The API automatically batches and chunks large inputs. If you hit a rate limit, your agent will receive an explicit 429 error code indicating exactly when to retry the request.

**If `list_jobs` shows a job failed, how do I retrieve the full error stack trace?**
The list function only provides status. You must then use specialized commands (like retrieving service metadata) and provide the specific Job ID to pull detailed logs and complete stack traces.

**Can I force `text_completion` to output structured data, like JSON?**
Yes, you instruct the model in your prompt. By defining a schema or explicitly requesting JSON format, you guide the underlying LLM to produce reliable, parsable code outputs.