How to Use the Granola MCP in OpenAI Agents SDK

Q: What is the fastest way to find a specific meeting note?

Use the searchdocuments MCP tool to run a full-text search across your workspace. This returns matching documents immediately, bypassing the need to paginate through the entire document list.

Run secure OpenAI Agents SDK pipelines that automatically pull meeting decisions and transcripts from Granola.

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Connect Granola MCP to OpenAI Agents SDK

Create your Vinkius account to connect Granola to OpenAI Agents SDK 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 Granola with OpenAI Agents SDK

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Run secure meeting pipelines with OpenAI Agents SDK

The `get_action_items` tool lets your agent pull verified post-meeting tasks directly into your production pipelines. This tool processes the raw meeting text so your agent can update external databases or trigger follow-up tasks without manual data entry. By chaining `get_summary` with your OpenAI agent's built-in guardrails, you ensure that only verified decisions pass to downstream systems. The SDK validates these actions before execution, keeping your production workflows clean and error-free.

Track transcript histories across multi-agent handoffs

The `get_transcript` tool retrieves full transcripts with speaker detection to feed your specialized analysis agents. Your master agent can hand off this raw text to a dedicated summarization agent to isolate specific technical requirements. Using `list_recent` alongside these handoffs lets your agent system maintain constant context without hitting token limits. The OpenAI dashboard traces every single tool call, so you see exactly when and why an agent requested a specific meeting transcript.

Batch process meeting data safely inside the sandbox

The `get_documents_batch` tool retrieves multiple meeting records in a single request to reduce your API latency. This batching mechanism helps your OpenAI agent analyze trends across multiple client calls without making repetitive network requests. Your agent runs these queries through a zero-trust Vinkius MCP Server sandbox. This isolated execution environment ensures your sensitive corporate records remain protected while the agent extracts metadata with `get_metadata`.

Setup guide

Set up Granola MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Granola tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Granola tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Granola tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Granola Agent",
            instructions="You have access to Granola tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Granola MCP in OpenAI Agents SDK

The SDK queries the Vinkius MCP Server endpoint using the MCPServerStreamableHttp client. It automatically registers all 12 tools, including list_documents and get_action_items, with no manual configuration required.

Yes, you can restrict tool execution by applying guardrails inside your agent definition. This prevents the agent from calling tools like search_documents unless specific validation conditions are met.

The SDK processes the text chunk by chunk to avoid exceeding model context windows. The MCP connection allows you to call get_summary first to check if you actually need to fetch the full transcript via get_transcript.

Use the search_documents MCP tool to run a full-text search across your workspace. This returns matching documents immediately, bypassing the need to paginate through the entire document list.

Your meeting transcripts, summaries, and action items are processed inside ephemeral V8 isolates. Vinkius never writes this data to persistent storage, ensuring absolute isolation from other users.

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