ChatFly MCP Server for Pydantic AIGive Pydantic AI instant access to 7 tools to Chat, Create Bot, Get Bot, and more
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect ChatFly 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 ChatFly app connector for Pydantic AI is a standout in the Customer Support category — giving your AI agent 7 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 ChatFly "
"(7 tools)."
),
)
result = await agent.run(
"What tools are available in ChatFly?"
)
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 ChatFly MCP Server
Connect your ChatFly account to any AI agent and take full control of your custom chatbot orchestration and automated knowledge ingestion workflows through natural conversation.
Pydantic AI validates every ChatFly tool response against typed schemas, catching data inconsistencies at build time. Connect 7 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
- Bot Orchestration — Create and manage multiple high-fidelity AI chatbot instances programmatically, including configuring welcome messages and internal metadata
- Knowledge Ingestion — Programmatically train your bots by uploading website URLs and documents to coordinate an accurate, data-driven knowledge base
- Real-Time Interaction — Send messages and retrieve AI responses from specific bots to test performance or integrate chat into custom business applications
- Source Management — Access and monitor your complete directory of data sources (URLs, docs) to oversee the information feeding your digital assistants
- Operational Monitoring — Track chatbot performance, session histories, and account-level status directly through your agent for instant reporting
The ChatFly MCP Server exposes 7 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 7 ChatFly tools available for Pydantic AI
When Pydantic AI connects to ChatFly through Vinkius, your AI agent gets direct access to every tool listed below — spanning chatbot-builder, conversational-ai, lead-qualification, 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.
Interact with a chatbot
Provide name and welcome message. Create a new chatbot
Get details of a specific bot
List all chatbots
List data sources for a bot
Update an existing bot
Add a knowledge source to a bot
Connect ChatFly to Pydantic AI via MCP
Follow these steps to wire ChatFly into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install Pydantic AI
pip install pydantic-aiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use Pydantic AI with the ChatFly MCP Server
Pydantic AI provides unique advantages when paired with ChatFly 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 ChatFly integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your ChatFly connection logic from agent behavior for testable, maintainable code
ChatFly + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the ChatFly MCP Server delivers measurable value.
Type-safe data pipelines: query ChatFly with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple ChatFly tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query ChatFly and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock ChatFly responses and write comprehensive agent tests
Example Prompts for ChatFly in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with ChatFly immediately.
"List all my available chatbots in ChatFly."
"Train 'bot_1' by ingesting 'https://vinkius.com/faq'."
"Ask 'bot_1': 'What are your support hours?'."
Troubleshooting ChatFly MCP Server with Pydantic AI
Common issues when connecting ChatFly to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiChatFly + Pydantic AI FAQ
Common questions about integrating ChatFly 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.