How to Use the Together AI MCP in LlamaIndex

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Connect Together AI MCP to LlamaIndex

Create your Vinkius account to connect Together AI to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Together AI with LlamaIndex

Ask AI about this MCP

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Claude

Perplexity

Indexing API Results into Vector Stores

The `generate_embeddings` tool is key here. When you run it, the resulting vectors are perfect for indexing into a vector store. You can use this to build a knowledge base from live API data. This means your RAG application queries actual Together AI outputs, not just general documents. Query past sessions or configurations and get answers grounded in real data.

Semantic Search Using MCP Server

You can index the results of `chat_completion` into your knowledge base. Instead of losing conversation history, it becomes searchable context. Your agent builds a unified index combining live API data with static documents. This lets you query things like 'What were the model IDs we used last week?'

Managing Model Knowledge for Retrieval

Need to know what models are available? Run `list_available_models`. You can then index this metadata so that when a user asks about capabilities, your agent pulls the specific list from the knowledge base. This is useful for building robust documentation tools where model context is critical for retrieval.

Setup guide

Set up Together AI MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Together AI MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Together AI tools.",
)
response = await agent.run("List recent Together AI data")

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Common questions about Together AI MCP in LlamaIndex

LlamaIndex makes the MCP tool output part of a searchable knowledge base. When you run `generate_embeddings`, the resulting vectors are what gets indexed for semantic search.

Yes, because the tool outputs become indexable data. You can build applications that answer questions about previous chat completions or API calls you've made.

The system is designed for this. It combines live data, like the output from `text_completion`, with traditional document resources into a single queryable index.

It touches text strings (from prompts and chat completions), model IDs, and structured metadata from listing tools.

The primary data type is textual content derived from tool execution. This includes chat messages, generated text, and descriptive prompts for images.

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