Can I see which invoices are still unpaid?

Yes! Use the `list_agency_invoices` tool. The agent will return all invoices, and you can ask it to filter for those with an 'Unpaid' or 'Overdue' status.

How do I check the status of a specific sales proposal?

Use the `list_sales_quotes` tool. Your agent will fetch all quotes and proposals, showing their current status (e.g., Sent, Viewed, Accepted).

Where do I find my Clientjoy API Token?

Log in to your Clientjoy account, navigate to **Settings**, and select **API**. You can generate and copy your API token from that section.

Clientjoy MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: Clientjoy
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Clientjoy 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 Clientjoy "
            "(8 tools)."
        ),
    )

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

asyncio.run(main())

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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 Clientjoy MCP Server

Connect your Clientjoy account to any AI agent and take full control of your agency operations through natural conversation. Streamline how you manage the entire lifecycle from lead capture to final invoicing natively.

Pydantic AI validates every Clientjoy tool response against typed schemas, catching data inconsistencies at build time. Connect 8 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

Lead Oversight — List and retrieve details for all sales leads and their capture status natively
Contact Intelligence — Access and monitor all client contacts and relationship history flawlessly
Invoicing Logistics — List all agency invoices and monitor their payment status flawlessly
Project Management — Access and monitor all client projects and their constituent tasks securely
Sales Pipelines — List and review quotes and proposals sent to potential clients flawlessly
Profile Visibility — Access your own user profile and core workspace metadata directly within your workspace flawlessly

The Clientjoy MCP Server exposes 8 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 Clientjoy to Pydantic AI via MCP

Follow these steps to integrate the Clientjoy 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 8 tools from Clientjoy with type-safe schemas

Why Use Pydantic AI with the Clientjoy MCP Server

Pydantic AI provides unique advantages when paired with Clientjoy 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 Clientjoy integration code

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

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

Clientjoy + Pydantic AI Use Cases

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

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

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

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

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

Clientjoy MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect Clientjoy to Pydantic AI via MCP:

get_contact_crm_details

Get detailed information for a specific contact

get_lead_crm_details

Get detailed information for a specific lead

get_my_clientjoy_profile

Retrieve information about the authenticated workspace user

list_agency_invoices

List all invoices and their payment status

list_agency_projects

List all client projects tracked in Clientjoy

list_clientjoy_contacts

List all contacts and clients stored in the CRM

list_clientjoy_leads

List all sales leads captured in Clientjoy

list_sales_quotes

List sales quotes and proposals sent to clients

Example Prompts for Clientjoy in Pydantic AI

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

"List all my new leads in Clientjoy."

"Show me my unpaid invoices."

"What is the status of the 'Website Redesign' project?"

Troubleshooting Clientjoy MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Clientjoy + Pydantic AI FAQ

Common questions about integrating Clientjoy 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 Clientjoy MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.