Granola MCP Server for Pydantic AI 12 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Granola through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Granola "
"(12 tools)."
),
)
result = await agent.run(
"What tools are available in Granola?"
)
print(result.data)
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 Granola MCP Server
Connect your Granola.ai account to any AI agent and take full control of your AI-powered meeting notes, searchable conversation memory, and automated summaries through natural conversation.
Pydantic AI validates every Granola tool response against typed schemas, catching data inconsistencies at build time. Connect 12 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
What you can do
- Meeting Document Orchestration — List all meeting documents in your workspace and retrieve primary entry points for workspace interactions natively
- Live Content Retrieval — Access full structured content of meeting documents, parsing human-modified annotations and ML-generated notes flawlessy
- AI Summarization — Retrieve synthesized AI-generated blocks reducing bulk meeting content into concise overviews and key takeaway nodes limitlessly
- Action Item Tracking — Isolate specifically categorized target steps inferred from recorded meeting intent to automate post-meeting follow-ups
- Transcript Auditing — Retrieve full speaker-detected transcripts parsed locally on device, containing semantic and chronological speech metadata natively
- Participant Navigation — Identify meeting attendees by cross-referencing calendar arrays bound to Granola sessions synchronously
- Global Workspace Search — Execute full-text term detection across all documents to find specific discussions and prioritized ranked datasets
- Folder & List Management — Enumerate high-level categorization labels grouping documents physically inside directories to browse your workspace hierarchy
- Batch Retrieval Oversight — Fetch multiple meeting documents by their IDs in a single request to analyze complex cross-meeting dependencies securely
The Granola MCP Server exposes 12 tools through the Vinkius. Connect it to Pydantic AI 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 Granola to Pydantic AI via MCP
Follow these steps to integrate the Granola MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
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 12 tools from Granola with type-safe schemas
Why Use Pydantic AI with the Granola MCP Server
Pydantic AI provides unique advantages when paired with Granola through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Granola integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Granola connection logic from agent behavior for testable, maintainable code
Granola + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Granola MCP Server delivers measurable value.
Type-safe data pipelines: query Granola with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Granola tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Granola and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Granola responses and write comprehensive agent tests
Granola MCP Tools for Pydantic AI (12)
These 12 tools become available when you connect Granola to Pydantic AI via MCP:
get_action_items
Extract action items identified from a meeting document
get_content
Retrieve the full structured content of a meeting document
get_documents_batch
Fetch multiple documents by their IDs in a single request
get_metadata
Retrieve metadata for a specific meeting document
get_participants
Retrieve the list of participants for a specific meeting
get_summary
Retrieve the AI-generated summary of a meeting document
get_transcript
Retrieve the full transcript of a meeting with speaker detection
list_by_date
List meeting documents within a specific date range
list_documents
List all meeting documents in the Granola workspace with pagination
list_folders
List all document lists (folders) in the Granola workspace
list_recent
List the 20 most recent meeting documents
search_documents
Full-text search across all meeting documents
Example Prompts for Granola in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Granola immediately.
"Show me the 5 most recent meeting documents"
"What were the action items from meeting 'abc-123'?"
"Search for meeting notes mentioning 'competitor analysis'"
Troubleshooting Granola MCP Server with Pydantic AI
Common issues when connecting Granola to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiGranola + Pydantic AI FAQ
Common questions about integrating Granola MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
Connect Granola 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 Granola to Pydantic AI
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.