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SmartChatAI MCP Server for Pydantic AIGive Pydantic AI instant access to 12 tools to Add Pdf To Knowledge Base, Add Text To Knowledge Base, Add Website To Knowledge Base, and more

Built by Vinkius GDPR 12 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect SmartChatAI 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 SmartChatAI app connector for Pydantic AI is a standout in the Industry Titans category — giving your AI agent 12 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 SmartChatAI "
            "(12 tools)."
        ),
    )

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

asyncio.run(main())
SmartChatAI
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 SmartChatAI MCP Server

Connect your SmartChatAI account to any AI agent to automate your intelligent chatbot orchestration and lead collection. SmartChatAI provides a premier platform for building custom AI bots, and this integration allows you to retrieve chatbot metadata, manage knowledge bases via URL or PDF, and track conversational history through natural conversation.

Pydantic AI validates every SmartChatAI 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

  • Chatbot Orchestration — List all managed AI bots and retrieve detailed profile metadata, including status and configuration programmatically.
  • Knowledge Base Lifecycle Management — Add new data sources (URL, PDF, Text) to your bots' knowledge base directly from the AI interface to ensure they are always informed.
  • Message & Reply Control — Send automated replies and retrieve detailed chat history to maintain high-quality customer interactions via natural language.
  • Web Scraper Automation — Trigger website scraping to ingest content into your AI models and ensure your bots have the latest information.
  • Operational Monitoring — Track system health, manage webhooks, and monitor bot activity to ensure your conversational platform is always optimized.

The SmartChatAI 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.

All 12 SmartChatAI tools available for Pydantic AI

When Pydantic AI connects to SmartChatAI through Vinkius, your AI agent gets direct access to every tool listed below — spanning chatbot-orchestration, knowledge-base, lead-collection, 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.

add_pdf_to_knowledge_base

Train bot using a PDF

add_text_to_knowledge_base

Train bot using raw text

add_website_to_knowledge_base

Train bot using a URL

check_api_health

Verify SmartChatAI API status

create_new_ai_bot

Requires a name and optional initial prompt. Provision a new AI agent

get_authenticated_user_profile

Get account profile

get_bot_chat_history

Retrieve conversation transcripts

get_chatbot_details

Get configuration for a specific bot

list_ai_chatbots

List all AI chatbots

list_configured_webhooks

List active webhooks

message_ai_chatbot

Send a message and get AI reply

scrape_domain_links

Discover and index domain links

Connect SmartChatAI to Pydantic AI via MCP

Follow these steps to wire SmartChatAI 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 12 tools from SmartChatAI with type-safe schemas

Why Use Pydantic AI with the SmartChatAI MCP Server

Pydantic AI provides unique advantages when paired with SmartChatAI 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 SmartChatAI 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 SmartChatAI connection logic from agent behavior for testable, maintainable code

SmartChatAI + Pydantic AI Use Cases

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

01

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

02

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

03

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

04

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

Example Prompts for SmartChatAI in Pydantic AI

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

01

"List all active AI bots in my SmartChatAI account."

02

"Show me all active chatbot conversations with their resolution rates and average response times."

03

"Train the chatbot with 10 new FAQ entries about our refund and return policies."

Troubleshooting SmartChatAI MCP Server with Pydantic AI

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

01

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

SmartChatAI + Pydantic AI FAQ

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