Together AI MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Together AI as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Together AI. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Together AI?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Together AI MCP Server
Connect your Together AI account to any AI agent and integrate bleeding-edge open-source models seamlessly into your workflow. Harness world-class inference speeds to query Llama, Mixtral, and more, or orchestrate specialized model fine-tuning jobs straight from your chat environment.
LlamaIndex agents combine Together AI tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Model Discovery — Explore and list all currently supported models on the Together network, identifying the best engine for any NLP or vision task
- Conversational AI — Run chat completion cycles on advanced models simply by supplying a model ID directly from the chat prompt
- Vector Storage Preparation — Generate instant rich embeddings for input texts, ready to populate your analytical databases
- Creative Media — Instruct external diffusion models to generate images using detailed physical descriptions
- Custom Fine-Tuning — Provision custom training runs by indicating a base framework and dataset file, alongside tracking existing job statuses
The Together AI MCP Server exposes 7 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Together AI to LlamaIndex via MCP
Follow these steps to integrate the Together AI MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 7 tools from Together AI
Why Use LlamaIndex with the Together AI MCP Server
LlamaIndex provides unique advantages when paired with Together AI through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Together AI tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Together AI tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Together AI, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Together AI tools were called, what data was returned, and how it influenced the final answer
Together AI + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Together AI MCP Server delivers measurable value.
Hybrid search: combine Together AI real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Together AI to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Together AI for fresh data
Analytical workflows: chain Together AI queries with LlamaIndex's data connectors to build multi-source analytical reports
Together AI MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Together AI to LlamaIndex via MCP:
chat_completion
Provide a model ID and a JSON array of messages. Executes a chat completion using Together AI models
create_finetune_job
Provide a base model ID and a training file ID. Creates a new fine-tuning job
generate_embeddings
Provide a model ID and a JSON array of strings. Generates vector embeddings for input texts
generate_image
Provide a model ID and descriptive prompt. Generates an image from a text prompt
list_available_models
Lists all AI models available on Together AI
list_finetune_jobs
Lists all fine-tuning jobs
text_completion
Provide a model ID and a prompt. Executes a base text completion
Example Prompts for Together AI in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Together AI immediately.
"List all the models currently available on Together AI."
"Generate an embedding array using model `togethercomputer/m2-bert-80M-8k-retrieval` for the sentence 'The cat sat on the mat'."
Troubleshooting Together AI MCP Server with LlamaIndex
Common issues when connecting Together AI to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpTogether AI + LlamaIndex FAQ
Common questions about integrating Together AI MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Together AI with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Together AI to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.