How do I get my Common Room API token?

Go to **Settings > API Tokens** in Common Room and generate a new token. Copy it and paste it into the field below.

What is a segment in Common Room?

A segment is a group of contacts or organizations defined by specific criteria or manually curated. They help you target and analyze subsets of your community.

Can I track custom activities?

Yes, but you first need to set up an API Signal in Common Room to receive custom activity data. Once configured, the agent can query those activities.

Common Room MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Common Room
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

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

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

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 Common Room "
            "(10 tools)."
        ),
    )

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

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Common Room MCP Server

Connect your AI assistant to Common Room, the intelligent community growth platform that helps organizations find and build relationships with community members.

Pydantic AI validates every Common Room tool response against typed schemas, catching data inconsistencies at build time. Connect 10 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 Search — Find community members by email, name, or external identity across connected platforms.
Segment Management — List all segments, view member counts, and add or remove contacts from specific cohorts.
Activity Tracking — Retrieve activity feeds to understand engagement patterns and identify key contributors.

The Common Room MCP Server exposes 10 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 Common Room to Pydantic AI via MCP

Follow these steps to integrate the Common Room 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 10 tools from Common Room with type-safe schemas

Why Use Pydantic AI with the Common Room MCP Server

Pydantic AI provides unique advantages when paired with Common Room 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 Common Room integration code

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

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

Common Room + Pydantic AI Use Cases

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

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

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

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

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

Common Room MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Common Room to Pydantic AI via MCP:

add_contact_to_segment

Manually add a contact to a specific segment

get_contact_by_email

Retrieve detailed information about a member by their email

get_contact_tags

Get tags associated with a specific member

get_organization_details

Retrieve details of a specific organization

get_segment_status

Retrieve status and member count for a specific segment

list_activity_types

Retrieve a list of supported activity types in Common Room

list_segment_members

List contacts that belong to a specific segment

list_segments

Retrieve a list of all segments in Common Room

search_contacts

Search for contacts/members in your Common Room

search_organizations

Search for organizations in Common Room

Example Prompts for Common Room in Pydantic AI

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

"Search for the member with email 'dev@example.com'."

"Show me all segments and their member counts."

"Add 'Alex Chen' to the 'Enterprise Leads' segment."

Troubleshooting Common Room MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Common Room + Pydantic AI FAQ

Common questions about integrating Common Room MCP Server with Pydantic AI.

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.

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.

Can I switch LLM providers without changing MCP code?

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