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folk MCP Server for Pydantic AIGive Pydantic AI instant access to 6 tools to Create Contact, Get Contact Details, List Contact Notes, and more

Built by Vinkius GDPR 6 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect folk through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Ask AI about this App Connector for Pydantic AI

The folk app connector for Pydantic AI is a standout in the Productivity category — giving your AI agent 6 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

python
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 folk "
            "(6 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in folk?"
    )
    print(result.data)

asyncio.run(main())
folk
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About folk MCP Server

Connect your folk CRM account to any AI agent and simplify how you manage your professional relationships, coordinate contact groups, and track interactions through natural conversation.

Pydantic AI validates every folk tool response against typed schemas, catching data inconsistencies at build time. Connect 6 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

  • Contact Oversight — List all contacts and retrieve detailed profile metadata, including custom fields and status.
  • Group Management — Coordinate your contact lists by querying and managing specific groups in your folk workspace.
  • Interaction Tracking — Retrieve a complete history of emails, meetings, and calls for any contact to maintain context.
  • Relationship CRM — Create new contact records and update profile data programmatically via AI.
  • Notes & Insights — List and query all notes and comments associated with your contacts to capture important details.
  • Operational Visibility — Verify account configurations and monitor your professional ecosystem directly from the agent.

The folk MCP Server exposes 6 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.

All 6 folk tools available for Pydantic AI

When Pydantic AI connects to folk through Vinkius, your AI agent gets direct access to every tool listed below — spanning contact-management, relationship-tracking, crm-automation, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

create_contact

Create a new contact

get_contact_details

Get details for a specific contact

list_contact_notes

List notes for a contact

list_contacts

Optionally filter by group ID. List folk contacts

list_groups

List folk groups

list_interactions

List interactions for a contact

Connect folk to Pydantic AI via MCP

Follow these steps to wire folk into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

01

Install Pydantic AI

Run pip install pydantic-ai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token
03

Run the agent

Save to agent.py and run: python agent.py
04

Explore tools

The agent discovers 6 tools from folk with type-safe schemas

Why Use Pydantic AI with the folk MCP Server

Pydantic AI provides unique advantages when paired with folk through the Model Context Protocol.

01

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

02

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your folk integration code

03

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

04

Dependency injection system cleanly separates your folk connection logic from agent behavior for testable, maintainable code

folk + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the folk MCP Server delivers measurable value.

01

Type-safe data pipelines: query folk with guaranteed response schemas, feeding validated data into downstream processing

02

API orchestration: chain multiple folk tool calls with Pydantic validation at each step to ensure data integrity end-to-end

03

Production monitoring: build validated alert agents that query folk and output structured, schema-compliant notifications

04

Testing and QA: use Pydantic AI's dependency injection to mock folk responses and write comprehensive agent tests

Example Prompts for folk in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with folk immediately.

01

"List all active contacts in my folk account."

02

"Show me the recent interactions for 'John Miller' (ID: 10293)."

03

"Add a new contact: 'Anna White' (anna@example.com)."

Troubleshooting folk MCP Server with Pydantic AI

Common issues when connecting folk to Pydantic AI through the Vinkius, and how to resolve them.

01

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

folk + Pydantic AI FAQ

Common questions about integrating folk MCP Server with Pydantic AI.

01

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.
02

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.
03

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your folk MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.