# How to integrate Supportbee MCP with Pydantic AI

```json
{
  "title": "How to integrate Supportbee MCP with Pydantic AI",
  "toolkit": "Supportbee",
  "toolkit_slug": "supportbee",
  "framework": "Pydantic AI",
  "framework_slug": "pydantic-ai",
  "url": "https://composio.dev/toolkits/supportbee/framework/pydantic-ai",
  "markdown_url": "https://composio.dev/toolkits/supportbee/framework/pydantic-ai.md",
  "updated_at": "2026-05-12T10:27:33.636Z"
}
```

## Introduction

This guide walks you through connecting Supportbee to Pydantic AI using the Composio tool router. By the end, you'll have a working Supportbee agent that can archive all tickets resolved this week, assign new tickets to the support team, create a reusable snippet for refund replies through natural language commands.
This guide will help you understand how to give your Pydantic AI agent real control over a Supportbee account through Composio's Supportbee MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Supportbee with

- [OpenAI Agents SDK](https://composio.dev/toolkits/supportbee/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/supportbee/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/supportbee/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/supportbee/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/supportbee/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/supportbee/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/supportbee/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/supportbee/framework/cli)
- [Google ADK](https://composio.dev/toolkits/supportbee/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/supportbee/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/supportbee/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/supportbee/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/supportbee/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/supportbee/framework/crew-ai)

## 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 Supportbee
- 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 Supportbee 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 Supportbee MCP server, and what's possible with it?

The Supportbee MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Supportbee account. It provides structured and secure access to your support ticketing system, so your agent can perform actions like creating and replying to tickets, managing team assignments, organizing tickets, and automating support workflows on your behalf.
- Automated ticket creation and updates: Instantly open new support tickets, update their content, or post replies to customer inquiries without leaving your workflow.
- Team assignment and ticket routing: Direct your agent to assign tickets to the right team or agent, ensuring every request is handled by the appropriate group.
- Archiving and deleting tickets: Keep your helpdesk organized by having the agent archive resolved tickets or permanently remove unwanted ones from the system.
- Reusable response snippets: Let your agent create, manage, and delete response templates so your team can reply faster and more consistently.
- Rule-based workflow automation: Empower your agent to create new automation rules that streamline ticket routing, escalation, and handling based on custom conditions.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `SUPPORTBEE_ADD_LABEL_TO_TICKET` | Add Label to Ticket | Tool to add a label to a ticket. Use when you need to categorize or tag a ticket with a specific label. The label must already exist in your SupportBee account before adding it to a ticket. |
| `SUPPORTBEE_ARCHIVE_TICKET` | Archive SupportBee Ticket | Tool to archive a SupportBee ticket by its ID. Use when you want to move resolved tickets to the archive. |
| `SUPPORTBEE_ASSIGN_TICKET_TO_TEAM` | Assign Ticket to Team | Assigns a ticket to a team in SupportBee. Use when you need to route a support ticket to a specific team for handling. Note: If the ticket is already assigned to a team and a user, reassigning to another team will remove the user assignee. |
| `SUPPORTBEE_CREATE_COMMENT` | Create Ticket Comment | Creates an internal comment on a ticket in SupportBee. Comments are private notes visible only to agents, not to customers. Use this to add internal notes, observations, or collaborate with team members on a ticket. |
| `SUPPORTBEE_CREATE_CONSEQUENCE` | Create Consequence | Creates a new consequence for rules automation in SupportBee. Use when setting up automated actions that should be triggered by rules (e.g., auto-assign tickets, archive, or mark as spam). |
| `SUPPORTBEE_CREATE_EMAIL` | Create Forwarding Email | Create a new forwarding email address for the company in SupportBee. Use this to add new support email addresses that will forward incoming emails to your SupportBee account as tickets. |
| `SUPPORTBEE_CREATE_FILTER` | Create Filter | Creates a filter in SupportBee by linking a rule with a consequence. Use this after creating both a rule (defining match conditions) and a consequence (defining actions to perform). |
| `SUPPORTBEE_CREATE_RULE` | Create Rule | Creates a new automation rule in SupportBee to automatically process tickets based on conditions. Rules allow you to automate ticket workflows by: - Matching tickets based on field conditions (subject, sender, body, etc.) - Automatically applying actions like labeling, archiving, assigning, or setting priority Use this after fetching available labels/teams to get valid IDs for actions. The rule will be evaluated for all new and existing tickets matching the conditions. Returns the created rule's unique ID. |
| `SUPPORTBEE_CREATE_SNIPPET` | Create Snippet | Create a reusable snippet (canned response) in SupportBee. Snippets are pre-written text templates that agents can quickly insert into ticket replies. Use this to create standard responses for common customer inquiries like refunds, FAQs, or welcome messages. |
| `SUPPORTBEE_CREATE_TICKET` | Create SupportBee Ticket | Creates a new support ticket in SupportBee with a subject, content, and requester details. Use this action to: - Create tickets from customer inquiries or issues - Assign tickets to specific agents or teams during creation - Add tags and labels for better ticket organization - Include CC recipients to keep stakeholders informed The ticket will be created in an unanswered state and will appear in the inbox unless marked as spam. |
| `SUPPORTBEE_CREATE_TICKET_REPLY` | Create Ticket Reply | Create a reply to a support ticket in SupportBee. Replies are sent to customers via email and are visible to them. Use this when you need to respond to a customer's ticket with information, updates, or solutions. Provide the ticket ID and HTML-formatted content for your reply. |
| `SUPPORTBEE_CREATE_USER_OR_CUSTOMER_GROUP` | Create SupportBee User | Invites a new user to your SupportBee account. The user will receive an email invitation and can be assigned as an agent (handles tickets), admin (full access), or collaborator (view/comment only). Use this when you need to add team members to your helpdesk programmatically. |
| `SUPPORTBEE_DELETE_SNIPPET` | Delete Snippet | Permanently delete a snippet by its ID from SupportBee. Use this action when you need to remove an unwanted or outdated snippet (canned response template). This action is destructive and cannot be undone. To find snippet IDs, use the 'Fetch Snippets' action first. |
| `SUPPORTBEE_DELETE_TICKET` | Delete SupportBee Ticket | Permanently delete a trashed ticket from SupportBee. The ticket must first be moved to trash using the Trash Ticket action before it can be permanently deleted. Only admins can delete trashed tickets. This action is irreversible. |
| `SUPPORTBEE_FETCH_EMAILS` | Fetch Forwarding Emails | Retrieve all forwarding email addresses configured for the company. Use this tool to list the support email addresses that forward emails to SupportBee. |
| `SUPPORTBEE_FETCH_LABELS` | Fetch SupportBee Labels | Tool to retrieve all custom labels. Use when you need to list labels for ticket categorization. |
| `SUPPORTBEE_FETCH_SNIPPETS` | Fetch Snippets | Fetches saved response snippets (canned responses/templates) from SupportBee. Snippets are reusable text templates that can be inserted into ticket replies. Use this to list available snippets for quick responses. |
| `SUPPORTBEE_FETCH_TEAMS` | Fetch SupportBee Teams | Retrieves all teams in the SupportBee account. Use this to list available teams before assigning tickets to teams or filtering tickets by team. Returns team IDs, names, descriptions, and timestamps. |
| `SUPPORTBEE_GET_AVG_FIRST_RESPONSE_TIME_REPORT` | Get Avg First Response Time Report | Tool to retrieve average first response time data points over time. Use when analyzing first-response performance metrics for support tickets. Returns time-series data with response times in seconds and Unix timestamps. Reports require admin API token access. Data is limited to a maximum 30-day window per request. |
| `SUPPORTBEE_GET_REPLIES_COUNT_REPORT` | Get Replies Count Report | Retrieves replies count report data for the company. Returns time-series data points showing the number of replies over time. The report provides aggregate metrics for the entire company account and includes type information (company/user/team), the entity ID, and the metric name. Requires admin-level API access. Use this to analyze reply volume trends and patterns. |
| `SUPPORTBEE_GET_TICKET` | Get Ticket | Tool to retrieve a specific SupportBee ticket by its ID. Returns complete ticket details including subject, content, requester, assignee, labels, and reply/comment counts. Use when you need to fetch full details of a single ticket. |
| `SUPPORTBEE_GET_TICKETS_COUNT_REPORT` | Get Tickets Count Report | Tool to get ticket count data points over time. Use when analyzing ticket volume trends within a specific date range. Supports optional filtering by agent, team, or label. |
| `SUPPORTBEE_LIST_TICKET_COMMENTS` | List Ticket Comments | Retrieves all internal comments (private agent notes) for a specific ticket. Comments are visible only to agents within the helpdesk, not to customers. Use this to review internal discussion history on a ticket. |
| `SUPPORTBEE_LIST_TICKET_REPLIES` | List Ticket Replies | Lists all replies on a specific support ticket in SupportBee. Returns reply content, replier details, timestamps, and attachments. Use this to view the conversation history on a ticket. Returns an empty list if the ticket has no replies yet. |
| `SUPPORTBEE_LIST_TICKETS` | List Tickets | Tool to list tickets from SupportBee. Returns a paginated list of tickets with optional filters for spam, trash, archived, assigned user/group, labels, and more. Use when you need to retrieve and browse tickets in the helpdesk. |
| `SUPPORTBEE_LIST_USERS` | List SupportBee Users | Retrieves all users and customer groups in your SupportBee company. Use this when you need to list team members, filter by user type (agents/admins vs customer groups), or include invited users who haven't confirmed their accounts yet. |
| `SUPPORTBEE_MARK_TICKET_AS_ANSWERED` | Mark SupportBee Ticket as Answered | Marks a SupportBee ticket as answered by adding the 'answered' status. Use this after sending a response to a customer to indicate the ticket has been addressed. This action is idempotent - calling it on an already answered ticket has no adverse effect. |
| `SUPPORTBEE_MARK_TICKET_AS_SPAM` | Mark SupportBee Ticket as Spam | Tool to mark a SupportBee ticket as spam. Use when you need to flag unwanted or malicious ticket submissions after obtaining the ticket ID. |
| `SUPPORTBEE_MARK_TICKET_AS_UNANSWERED` | Mark SupportBee Ticket as Unanswered | Marks a SupportBee ticket as unanswered by removing its 'answered' status. Use this to revert a ticket's status after it was previously marked as answered, typically when additional follow-up is needed from the support team. This action is idempotent - calling it on an already unanswered ticket has no adverse effect. |
| `SUPPORTBEE_REMOVE_LABEL_FROM_TICKET` | Remove Label From Ticket | Tool to remove a label from a ticket. Use when you need to unlabel or uncategorize a ticket by removing an existing label. |
| `SUPPORTBEE_SEARCH_TICKETS` | Search SupportBee Tickets | Tool to search SupportBee tickets. Use when you need to find tickets by query with pagination. |
| `SUPPORTBEE_SHOW_TICKET_REPLY` | Show Ticket Reply | Tool to fetch a specific reply for a SupportBee ticket. Use when you need details of a single reply by ticket and reply IDs. |
| `SUPPORTBEE_SHOW_USER_OR_CUSTOMER_GROUP` | Show SupportBee User or Customer Group | Retrieves details of a SupportBee user (agent/admin) or customer group by their ID. Use this action when you need to fetch profile information like name, email, role, or timestamps for a specific user whose ID you already have (e.g., from a ticket response). |
| `SUPPORTBEE_TRASH_TICKET` | Trash SupportBee Ticket | Tool to trash a SupportBee ticket by its ID. Use when you need to remove a ticket into the trash folder. |
| `SUPPORTBEE_UNARCHIVE_TICKET` | Unarchive SupportBee Ticket | Tool to unarchive a SupportBee ticket by its ID. Use when you need to restore an archived ticket back to active status. |
| `SUPPORTBEE_UNASSIGN_TICKET_FROM_TEAM` | Unassign Ticket from Team | Tool to un-assign a ticket from its assigned team. Use when you need to remove the current team ownership before reassigning or closing the ticket. |
| `SUPPORTBEE_UNASSIGN_TICKET_FROM_USER` | Unassign User From Ticket | Tool to un-assign a ticket from its assigned user/agent. Use when you need to remove the current user ownership before reassigning to a different user or closing the ticket. |
| `SUPPORTBEE_UNMARK_TICKET_AS_SPAM` | Unmark SupportBee Ticket as Spam | Tool to unmark a SupportBee ticket as spam. Use when a ticket was incorrectly flagged as spam. |
| `SUPPORTBEE_UNTRASH_TICKET` | Untrash SupportBee Ticket | Restores a trashed SupportBee ticket back to active status. Use when you need to recover a ticket that was previously moved to trash. |
| `SUPPORTBEE_UPDATE_SNIPPET` | Update Snippet | Update an existing snippet (canned response) in SupportBee. Use this to modify the name, content, or tags of a snippet. To find snippet IDs, use the 'Fetch Snippets' action first. |
| `SUPPORTBEE_UPDATE_USER` | Update SupportBee User | Update an existing SupportBee user's profile information including name, email, role, avatar, or signature. This action modifies user account details via the SupportBee API. You can update one or multiple fields in a single request. Commonly used to change user roles (agent/admin), update contact information, or customize user profiles. Requirements: - Valid user ID (obtain from SUPPORTBEE_CREATE_USER_OR_CUSTOMER_GROUP or other user-related actions) - At least one field to update (name, email, role, avatar_url, or signature) Common use cases: - Promote an agent to admin by updating the role field - Update user email addresses when they change - Customize user signatures for support ticket replies |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Supportbee MCP server is an implementation of the Model Context Protocol that connects your AI agent to Supportbee. It provides structured and secure access so your agent can perform Supportbee operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 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

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

Install the required libraries.
What's happening:
- composio connects your agent to external SaaS tools like Supportbee
- pydantic-ai lets you create structured AI agents with tool support
- python-dotenv loads your environment variables securely from a .env file
```bash
pip install composio pydantic-ai python-dotenv
```

### 3. Set up environment variables

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
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key
```

### 4. Import dependencies

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

### 5. Create a Tool Router Session

What's happening:
- We're creating a Tool Router session that gives your agent access to Supportbee 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
```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 Supportbee
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["supportbee"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
```

### 6. Initialize the Pydantic AI Agent

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

### 7. Build the chat interface

What's happening:
- The agent reads input from the terminal and streams its response
- Supportbee API calls happen automatically under the hood
- The model keeps conversation history to maintain context across turns
```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 Supportbee.\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()
```

### 8. Run the application

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

## Complete Code

```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 Supportbee
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["supportbee"],
    )
    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
    supportbee_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[supportbee_mcp],
        instructions=(
            "You are a Supportbee assistant. Use Supportbee 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 Supportbee.\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 Supportbee through Composio's Tool Router. With this setup, your agent can perform real Supportbee 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 + Supportbee for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

## How to build Supportbee MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/supportbee/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/supportbee/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/supportbee/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/supportbee/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/supportbee/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/supportbee/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/supportbee/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/supportbee/framework/cli)
- [Google ADK](https://composio.dev/toolkits/supportbee/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/supportbee/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/supportbee/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/supportbee/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/supportbee/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/supportbee/framework/crew-ai)

## Related Toolkits

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- [Callpage](https://composio.dev/toolkits/callpage) - Callpage is a lead capture platform that lets businesses instantly connect with website visitors via callback. It boosts lead generation and increases your sales conversion rates.
- [Clearout](https://composio.dev/toolkits/clearout) - Clearout is an AI-powered service for verifying, finding, and enriching email addresses. It boosts deliverability and helps you discover high-quality leads effortlessly.
- [Clientary](https://composio.dev/toolkits/clientary) - Clientary is a platform for managing clients, invoices, projects, proposals, and more. It streamlines client work and saves you serious admin time.
- [Convolo ai](https://composio.dev/toolkits/convolo_ai) - Convolo ai is an AI-powered communications platform for sales teams. It accelerates lead response and improves conversion rates by automating calls and integrating workflows.
- [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment.
- [Emelia](https://composio.dev/toolkits/emelia) - Emelia is an all-in-one B2B prospecting platform for cold-email, LinkedIn outreach, and prospect research. It streamlines outbound campaigns so you can find, engage, and warm up leads faster.
- [Findymail](https://composio.dev/toolkits/findymail) - Findymail is a B2B data provider offering verified email and phone contacts for sales prospecting. Enhance outreach with automated exports, email verification, and CRM enrichment.
- [Freshdesk](https://composio.dev/toolkits/freshdesk) - Freshdesk is customer support software with ticketing and automation tools. It helps teams streamline helpdesk operations for faster, better customer support.
- [Fullenrich](https://composio.dev/toolkits/fullenrich) - FullEnrich is a B2B contact enrichment platform that aggregates emails and phone numbers from 15+ data vendors. Instantly find and verify lead contact data to boost your outreach.
- [Gatherup](https://composio.dev/toolkits/gatherup) - GatherUp is a customer feedback and online review management platform. It helps businesses boost their reputation by streamlining how they collect and manage customer feedback.
- [Getprospect](https://composio.dev/toolkits/getprospect) - Getprospect is a business email discovery tool with LinkedIn integration. Use it to quickly find and verify professional email addresses.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Supportbee MCP?

With a standalone Supportbee MCP server, the agents and LLMs can only access a fixed set of Supportbee tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Supportbee and many other apps based on the task at hand, all through a single MCP endpoint.

### Can I use Tool Router MCP with Pydantic AI?

Yes, you can. Pydantic AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Supportbee tools.

### Can I manage the permissions and scopes for Supportbee while using Tool Router?

Yes, absolutely. You can configure which Supportbee scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

### How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Supportbee data and credentials are handled as safely as possible.

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[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)
