Grain 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 Grain 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 Grain "
"(12 tools)."
),
)
result = await agent.run(
"What tools are available in Grain?"
)
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 Grain MCP Server
Connect your Grain.com account to any AI agent and take full control of your team meeting recordings, automated transcriptions, and AI-powered insights through natural conversation.
Pydantic AI validates every Grain 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 Orchestration — List all meeting recordings in your workspace and retrieve primary entry points for workspace interactions natively
- Live Detail Retrieval — Resolve deep specific objects including transcripts and speaker attribution mapped by recording ID flawlessly
- AI Transcription — Download full text structures with speaker attribution, parsing raw linguistic data to review critical discussions limitlessly
- Contextual Insights — Extract high-level abstract reductions including sentiment mapping, summaries, and key takeaways generated by Grain's ML engines
- Action Item Tracking — Filter targeted follow-up tasks detected automatically within meeting scopes to automate post-call workflows
- Highlight Navigation — Identify curated clips and key moments generated by users within specific timestamps to focus on critical insights
- Global Search — Execute keyword scanning across all meeting recordings to find specific discussions and ranked datasets synchronously
- Asset Ingestion — Ingest remote video streams by passing public URLs for initial structural transformations and AI processing securely
- Team Oversight — Retrieve fully enumerated team maps tracking workspace members and authenticated user profiles natively
The Grain 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 Grain to Pydantic AI via MCP
Follow these steps to integrate the Grain 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 Grain with type-safe schemas
Why Use Pydantic AI with the Grain MCP Server
Pydantic AI provides unique advantages when paired with Grain 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 Grain integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Grain connection logic from agent behavior for testable, maintainable code
Grain + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Grain MCP Server delivers measurable value.
Type-safe data pipelines: query Grain with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Grain tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Grain and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Grain responses and write comprehensive agent tests
Grain MCP Tools for Pydantic AI (12)
These 12 tools become available when you connect Grain to Pydantic AI via MCP:
get_action_items
Extract all action items identified from a recording
get_current_user
Retrieve the authenticated Grain user profile
get_insights
Retrieve AI-generated insights from a recording
get_recording
Retrieve full details of a specific meeting recording
get_transcript
Retrieve the full timestamped transcript of a meeting with speaker names
list_highlights
List all highlights (curated clips) from a recording
list_recordings
List all meeting recordings in the Grain workspace
list_shared_clips
List all clips that have been shared from the workspace
list_tags
List all tags used across recordings and highlights
list_workspace_members
List all members of the Grain workspace
search_recordings
Search across all meeting recordings by keyword
upload_video
Upload an external video URL for processing by Grain
Example Prompts for Grain in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Grain immediately.
"List my meeting recordings from today"
"What were the key decisions in the 'Roadmap Sync' meeting?"
"Search for recordings mentioning 'pricing strategy'"
Troubleshooting Grain MCP Server with Pydantic AI
Common issues when connecting Grain to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiGrain + Pydantic AI FAQ
Common questions about integrating Grain 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 Grain 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 Grain to Pydantic AI
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.