How to Use the Groq MCP in LlamaIndex

Q: How do I connect LlamaIndex to the Groq MCP Server?

Install llama-index-tools-mcp via pip, initialize BasicMCPClient with the HTTP URL, and convert it using McpToolSpec. You can then pass the output of totoollistasync() directly to your FunctionAgent.

Q: Can LlamaIndex query past Groq completions stored in vector memory?

Yes, because LlamaIndex indexes the output of chatcompletion directly into your vector store. Your agent can perform semantic searches over previous run histories to ground its answers in actual, verified API data.

Q: Does the Groq integration support structured JSON outputs for LlamaIndex parsing?

Yes, the structuredoutput tool forces the LPU engine to return structured JSON that matches your exact schemas. LlamaIndex uses this to extract clean metadata fields directly from raw documents without parsing errors.

Q: How do I check model specifications inside my LlamaIndex application?

Your agent can call getmodel to verify context window sizes and performance metrics before executing a query. This prevents your RAG pipeline from sending oversized payloads that exceed the active model's token limits.

Index ultra-fast Groq LPU inference outputs directly into your LlamaIndex vector stores using this fast MCP Server.

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Connect Groq MCP to LlamaIndex

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Setup Groq with LlamaIndex

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Feed LPU completions to LlamaIndex vector stores

The `chat_completion` tool generates lightning-fast responses that your LlamaIndex pipeline can index as fresh document nodes. This speeds up your agentic RAG loops by stripping away the latency bottleneck of traditional LLM providers. By running this MCP Server, your query engine can dynamically pull live context, run a completion, and index the output in a single async pass. You get grounded answers without the typical 3-second wait times.

Generate and store embeddings in LlamaIndex

The `create_embedding` tool outputs high-quality vector representations of your text directly into LlamaIndex index structures. This lets you build searchable knowledge bases where live API data and documents live in a unified index. When new data arrives, your indexer checks the source models using `list_models` and `get_model` to verify compatibility. The entire process runs through the secure MCP connection, keeping your vector generation pipeline clean and direct.

Ingest and translate audio inside LlamaIndex RAG

The `transcribe_audio` tool converts voice inputs into indexable documents that LlamaIndex can immediately ingest into its vector store. For non-English audio, the `translate_audio` tool translates the spoken words into English text before indexing. This setup removes the need for separate transcription pipelines in your RAG workflow. Your agent can run `moderate_content` on the transcribed text first, ensuring only safe, clean data gets written to your persistent vector database.

Setup guide

Set up Groq 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 Groq 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 Groq tools.",
)
response = await agent.run("List recent Groq data")

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Common questions about Groq MCP in LlamaIndex

Install `llama-index-tools-mcp` via pip, initialize `BasicMCPClient` with the HTTP URL, and convert it using `McpToolSpec`. You can then pass the output of `to_tool_list_async()` directly to your `FunctionAgent`.

Yes, because LlamaIndex indexes the output of `chat_completion` directly into your vector store. Your agent can perform semantic searches over previous run histories to ground its answers in actual, verified API data.

Yes, the `structured_output` tool forces the LPU engine to return structured JSON that matches your exact schemas. LlamaIndex uses this to extract clean metadata fields directly from raw documents without parsing errors.

Your agent can call `get_model` to verify context window sizes and performance metrics before executing a query. This prevents your RAG pipeline from sending oversized payloads that exceed the active model's token limits.

Your raw text prompts, document embeddings, and audio payloads are processed inside a zero-trust, ephemeral V8 sandbox. Vinkius isolates each connection, preventing your proprietary knowledge base inputs from being leaked or saved to external storage.

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