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How to integrate Docsbot ai MCP with Pydantic AI

Connect Pydantic AI to Docsbot ai MCP. List all bots for your team, generate support ticket from recent chat, and more using natural language, with authentication handled for you.

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Api Key

Docsbot ai is a platform that lets you build custom AI chatbots trained on your documentation. It automates customer support and content generation, saving time and improving response quality.

38 Tools

Introduction

This guide walks you through connecting Docsbot ai to Pydantic AI using the Composio tool router. By the end, you'll have a working Docsbot ai agent that can create and update bots, add training sources, search bot content, review conversations and leads, and track bot statistics through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Docsbot ai account through Composio's Docsbot ai MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Docsbot ai with

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TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Docsbot ai
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Docsbot ai workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

What is the Docsbot ai MCP server, and what's possible with it?

The Docsbot ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Docsbot ai account. It provides structured and secure access to your Docsbot ai bots, teams, and conversation data, so your agent can perform actions like creating bots, managing teams, generating support tickets, and analyzing user questions on your behalf.

  • Custom bot creation and management: Instantly create new Docsbot ai bots or update existing ones, letting your agent provision and configure bots for different documentation needs.
  • Team administration and overview: Allow your agent to fetch details about your teams or list all teams associated with your account, making it easier to manage collaboration and bot access.
  • Automated support ticket generation: Easily convert chatbot conversations into structured support tickets, so your agent can help streamline customer support and issue tracking.
  • Bot question and source analytics: Retrieve lists of questions asked to your bots or review all data sources connected to a given bot, empowering your agent to surface insights or monitor bot effectiveness.
  • Seamless bot and data cleanup: Direct your agent to delete bots or manage bot sources, helping you keep your Docsbot ai environment tidy and up to date.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Docsbot ai
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Docsbot ai
  • MCPServerStreamableHTTP connects to the Docsbot ai MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Docsbot ai
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["docsbot_ai"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Docsbot ai tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
docsbot_ai_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[docsbot_ai_mcp],
    instructions=(
        "You are a Docsbot ai assistant. Use Docsbot ai tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Docsbot ai endpoint
  • The agent uses GPT-5 to interpret user commands and perform Docsbot ai operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Docsbot ai.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Docsbot ai API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Docsbot ai and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Docsbot ai
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["docsbot_ai"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    docsbot_ai_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[docsbot_ai_mcp],
        instructions=(
            "You are a Docsbot ai assistant. Use Docsbot ai tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Docsbot ai.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Docsbot ai through Composio's Tool Router. With this setup, your agent can perform real Docsbot ai actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Docsbot ai for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

Every Docsbot ai action and event your agent gets out of the box.

Capture Conversation Lead

Tool to capture lead information by updating conversation metadata and saving the lead.

Create Bot

Tool to create a new bot within a team.

Create Bot Source

Tool to create a new source for a bot.

Create Webhook

Tool to create a new webhook subscription for a bot.

Delete Bot

Tool to delete a specific bot by its ID.

Delete Conversation

Tool to delete a specific conversation by its ID.

Delete Lead

Tool to delete a specific lead by ID.

Delete Question

Tool to delete a specific question from history.

Delete Source

Tool to delete a specific source from a bot by its ID.

Delete Webhook

Tool to delete a webhook (unsubscribe) by its ID.

Generate Conversation Ticket

Generates a structured support ticket from a Chat Agent conversation.

Get Bot Details

Tool to fetch details of a specific bot by ID within a team.

Get Bot Monthly Reports

Tool to retrieve monthly statistical reports for a bot.

Get Bot Statistics

Tool to retrieve comprehensive statistics and analytics for a bot over a time period or date range.

Get Source Details

Tool to retrieve detailed information about a specific source by its ID.

Get Team Details

Tool to fetch details of a specific team by its ID.

Get Upload URL

Get a presigned upload URL for uploading files as sources.

Get Webhook Details

Tool to retrieve details of a specific webhook by ID.

List Team Bots

List all bots for a given team.

List Bot Conversations

Tool to list conversation history for a bot with pagination.

List Bot Leads

Tool to list captured leads for a bot with pagination and date filtering.

List Questions

Tool to list all questions asked of a specific bot.

List Research Jobs

Tool to list all deep research jobs for a bot with pagination support.

List Bot Sources

Retrieves a paginated list of all sources for a specific bot within a team.

List Team Members

Tool to list all members of a team including their roles.

List Teams

Tool to list all teams.

List Bot Webhooks

List all registered webhooks for a bot.

Rate Answer

Tool to rate an answer from chat APIs as positive (1), neutral (0), or negative (-1).

Refresh Source

Tool to refresh a source to re-index its content.

Semantic Search Bot Content

Tool to perform semantic search on a bot's indexed content.

Test Escalated Webhook

Tool to trigger a test delivery of the conversation.

Test Lead Webhook

Tool to trigger a test lead webhook delivery.

Test Research Webhook

Tool to trigger a deep research webhook delivery test.

Trigger Rated Webhook Test

Tool to trigger a conversation.

Update Bot

Update a bot's configuration settings such as name, description, model, temperature, and appearance.

Update Team

Tool to update specific fields for a team.

Update Webhook

Update a bot webhook's target URL, subscribed events, label, or expiration date.

Upload File to Cloud Storage

Upload a file to cloud storage via a presigned URL.

FRAMEWORKS

How to build Docsbot ai MCP Agent with another framework

Claude Code logo

Claude Code

Use Docsbot ai MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Docsbot ai MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Docsbot ai MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Docsbot ai MCP with Claude Agent SDK

Codex logo

Codex

Use Docsbot ai MCP with Codex

OpenClaw logo

OpenClaw

Use Docsbot ai MCP with OpenClaw

Hermes logo

Hermes

Use Docsbot ai MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Docsbot ai MCP with Atomic Agent

Google ADK logo

Google ADK

Use Docsbot ai MCP with Google ADK

LangChain logo

LangChain

Use Docsbot ai MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Docsbot ai MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Docsbot ai MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Docsbot ai MCP with LlamaIndex

CrewAI logo

CrewAI

Use Docsbot ai MCP with CrewAI

AutoGen logo

AutoGen

Use Docsbot ai MCP with AutoGen

FAQ

Frequently asked questions

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