Can I train my bot on a new PDF file through the agent?

You can trigger the training process using the `trigger_bot_training` tool once you have added files via the ChatFly dashboard. Future updates will support direct file uploads.

How do I see the history of a specific conversation?

Use the `get_conversation_history` tool with the unique conversation ID. Your agent will fetch the full transcript of messages between the user and the bot.

Where do I find my ChatFly API Key?

Log in to your ChatFly dashboard and navigate to the 'API' or 'Integrations' section. You can generate and copy your API key from there.

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

Name: CHATFLY
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 CHATFLY 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 CHATFLY "
            "(8 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in CHATFLY?"
    )
    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 CHATFLY MCP Server

Connect your CHATFLY account to any AI agent and take full control of your custom chatbot workflows through natural conversation. Train and monitor your own AI agents using your business data.

Pydantic AI validates every CHATFLY 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

Chatbot Oversight — List and retrieve details for all custom AI chatbots in your account natively
Knowledge Logistics — List all uploaded documents and data sources used for bot training flawlessly
Training Automation — Trigger the training process for your chatbots to ingest new data securely
Conversation Intelligence — Access recent chat conversations and full message history flawlessly
Live Messaging — Send messages to your chatbots and receive AI-generated responses in real-time
System Monitoring — Retrieve core account information and monitor your AI usage quotas directly within your workspace

The CHATFLY 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 CHATFLY to Pydantic AI via MCP

Follow these steps to integrate the CHATFLY 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 CHATFLY with type-safe schemas

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

CHATFLY MCP Tools for Pydantic AI (8)

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

get_chatbot_details

Get detailed information for a specific chatbot

get_chatfly_account_info

Retrieve core account and quota information

get_conversation_history

Retrieve the message history for a specific conversation

list_chatfly_bots

List all AI chatbots in your account

list_fly_conversations

List recent chat conversations

list_uploaded_documents

List all files uploaded to the knowledge base

send_bot_message

Send a message to a chatbot and receive a response

trigger_bot_training

Trigger the training process for a chatbot

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 active chatbots in CHATFLY."

"Show me the last 5 conversations for bot 'Support Assistant'."

"Send a test message to bot ID 123: 'How do I reset my password?'"

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

Update: pip install --upgrade pydantic-ai

CHATFLY + Pydantic AI FAQ

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