How to Use the Magic Minutes MCP in LlamaIndex

Q: How do I load Magic Minutes tools into LlamaIndex?

Install llama-index-tools-mcp. Set up a BasicMCPClient with your Vinkius MCP endpoint, wrap it in an McpToolSpec, and await totoollistasync().

Q: Can LlamaIndex filter which Magic Minutes tools the agent uses?

Yes. You pass an allowedtools array to the tool spec. Restrict the agent to listcontacts and getcurrentuser if it only needs directory access.

Index and query your meeting transcripts with LlamaIndex and the Magic Minutes MCP Server.

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Connect Magic Minutes MCP to LlamaIndex

Create your Vinkius account to connect Magic Minutes 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 Magic Minutes with LlamaIndex

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Index Magic Minutes Transcripts

The `get_meeting_minutes` tool feeds raw dialogue directly into your LlamaIndex vector store. Your agent pulls the text, chunks it, and creates a searchable knowledge base. You stop guessing what was said and start querying actual records. This changes how you interact with past discussions. Instead of manually reading through notes, you ask your agent a question. It searches the index, finds the exact quote, and grounds its answer in hard data rather than hallucinations.

Build RAG Applications for Meetings

Using `list_meetings` and `get_meeting_details`, your application builds a timeline of decisions. The agent pulls the metadata, identifies the context, and enriches your RAG pipeline. You link this data with your internal documents. When a user asks why a specific project shifted direction, the agent cross-references the meeting notes with your codebase or wikis. The MCP Server provides the live API connection to make that happen.

Sync Tasks to Your Knowledge Base

The `list_tasks` tool extracts every pending action item from your workspace using this MCP integration. Your agent indexes these tasks alongside the meeting minutes. You immediately see the relationship between a conversation and the resulting work. If someone wants an update, the agent queries the task status via `get_task_details` and compares it to the original agenda.

Setup guide

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

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Common questions about Magic Minutes MCP in LlamaIndex

Install `llama-index-tools-mcp`. Set up a `BasicMCPClient` with your Vinkius MCP endpoint, wrap it in an `McpToolSpec`, and await `to_tool_list_async()`.

Yes. You pass an `allowed_tools` array to the tool spec. Restrict the agent to `list_contacts` and `get_current_user` if it only needs directory access.

The framework treats the output as a data resource. It embeds the JSON from tools like `list_folders` and stores it for semantic search.

The agent might not have indexed recent changes. You need to trigger a fresh call to `list_tasks` to update the vector store before running the query.

Meeting agendas and action items flow through a zero-trust, ephemeral sandbox. Authentication requires a single endpoint token, and the connection drops immediately after the read request finishes.

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