# How to integrate Bugherd MCP with LangChain

```json
{
  "title": "How to integrate Bugherd MCP with LangChain",
  "toolkit": "Bugherd",
  "toolkit_slug": "bugherd",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/bugherd/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/bugherd/framework/langchain.md",
  "updated_at": "2026-05-12T10:04:27.667Z"
}
```

## Introduction

This guide walks you through connecting Bugherd to LangChain using the Composio tool router. By the end, you'll have a working Bugherd agent that can list all active bugherd projects, create a new project for website feedback, add a comment to task by id through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Bugherd account through Composio's Bugherd MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Bugherd with

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

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Connect your Bugherd project to Composio
- Create a Tool Router MCP session for Bugherd
- Initialize an MCP client and retrieve Bugherd tools
- Build a LangChain agent that can interact with Bugherd
- Set up an interactive chat interface for testing

## What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.
Key features include:
- Agent Framework: Build agents that can use tools and make decisions
- MCP Integration: Connect to external services through Model Context Protocol adapters
- Memory Management: Maintain conversation history across interactions
- Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

## What is the Bugherd MCP server, and what's possible with it?

The Bugherd MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Bugherd account. It provides structured and secure access to your Bugherd workspace, so your agent can perform actions like creating tasks, managing projects, posting comments, and inviting team members—all on your behalf.
- Visual bug reporting and task creation: Instantly add new tasks to any project, capturing detailed bug reports or website feedback directly from your team or clients.
- Project management and workflow customization: Create new projects, add workflow columns, and delete projects when they’re no longer needed to keep your bug tracking organized and up-to-date.
- Collaboration and discussion: Add comments to tasks, attach files, and keep all stakeholders in the loop with contextual feedback and documentation.
- Team and guest access management: Seamlessly invite members or guests to projects so the right people can track, manage, and resolve issues together.
- Webhook automation and notifications: Set up webhooks to receive real-time notifications for events like task creation or new comments, helping you automate downstream workflows.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `BUGHERD_ADD_GUEST_TO_PROJECT` | Add Guest to Project | Tool to add a guest (client) to a project. Use when you want to add an existing client by ID or invite a new client by email. |
| `BUGHERD_ADD_MEMBER_TO_PROJECT` | Add Member to Project | Tool to add a member to a project in BugHerd. Use when you need to add an existing user to a specific project. |
| `BUGHERD_CREATE_ATTACHMENT` | Create Attachment | Tool to add a new attachment to a task using an existing URL. Use when you have project and task IDs and the external file URL ready. |
| `BUGHERD_CREATE_COLUMN` | Create Column | Tool to create a new column in a project. Use when you need to add a custom workflow column after identifying the project ID. |
| `BUGHERD_CREATE_COMMENT` | Create Comment | Tool to add a new comment to a task. Use when you need to record discussion or feedback on an existing task. |
| `BUGHERD_CREATE_PROJECT` | Create Project | Tool to create a new project. Use when you need to initialize a project after gathering its name and URL. Example: "Create a new project named 'My Website' with URL 'http://www.example.com'." |
| `BUGHERD_CREATE_TASK` | Create Task | Tool to add a new task in a project. Use when you have the project ID and full task details ready. |
| `BUGHERD_CREATE_WEBHOOK` | Create Webhook | Tool to create a new webhook for real-time event notifications. Use when you need to configure a callback endpoint for task or comment events. Example: "Create a webhook for 'task_create' events to be sent to 'https://example.com/webhook'." |
| `BUGHERD_DELETE_PROJECT` | Delete Project | Tool to delete a project. Use when you need to permanently remove a project and its associated data. This action cannot be undone, so confirm the project ID before calling. |
| `BUGHERD_LIST_ACTIVE_PROJECTS` | List Active Projects | Tool to list all active projects in your BugHerd account. Use when you need to retrieve the active projects list (e.g., for syncing or reporting). |
| `BUGHERD_LIST_ATTACHMENTS` | List Attachments | Tool to list all attachments for a task. Use when you need to retrieve file attachments after fetching task details. |
| `BUGHERD_LIST_COLUMNS` | List Columns | Tool to list all columns for a project. Use when you need the full set of default and custom columns for a project. |
| `BUGHERD_LIST_PROJECTS` | List Projects | Retrieves a paginated list of all projects in your BugHerd account. Returns project details including ID, name, creation date, owner, task status, and associated website URLs. Results are paginated with up to 100 projects per page. Use the meta.count field to determine the total number of projects. |
| `BUGHERD_LIST_PROJECT_TASKS` | List Project Tasks | Tool to list tasks within a specific BugHerd project with optional server-side filters (status/column, assignee, tag, priority, date filters) and pagination. Use when you need to retrieve tasks scoped to a single project. |
| `BUGHERD_LIST_USERS` | List Users | Tool to list all users in your account. Use after authenticating to fetch the current user roster. Supports pagination via the `page` parameter. |
| `BUGHERD_LIST_WEBHOOKS` | List Webhooks | Tool to list all installed webhooks. Use when you need to audit or verify existing webhooks after setup. |
| `BUGHERD_SHOW_ATTACHMENT` | Show Attachment | Tool to retrieve details of a specific attachment. Use after you have project_id, task_id, and attachment_id to get filename, URL, and timestamps. |
| `BUGHERD_SHOW_COLUMN` | Show Column | Tool to show details of a specific column. Use when you need metadata for a particular column within a project. |
| `BUGHERD_SHOW_ORGANIZATION` | Show Organization | Tool to retrieve your BugHerd organization details. Use after authenticating to fetch account metadata. |
| `BUGHERD_SHOW_PROJECT` | Show Project Details | Retrieves full details of a specific BugHerd project by ID. Returns comprehensive project information including name, settings, team members, guests, and kanban columns. Use this when you need detailed project data such as member lists, workflow columns, or project configuration settings. Requires a valid project_id obtained from list_projects or another source. |
| `BUGHERD_SHOW_USER_PROJECTS` | Show User Projects | Tool to list all projects a specific user has access to. Use after obtaining the user's ID. |
| `BUGHERD_SHOW_USER_TASKS` | Show User Tasks | Retrieves all tasks created by or assigned to a specific user, grouped by project. Returns task details including ID, description, priority, status, timestamps, and tags. Requires a valid user_id (obtain from List Users action). Supports pagination via page parameter. |
| `BUGHERD_UPDATE_COLUMN` | Update Column | Tool to update a column in a project. Use when you have the project and column IDs and need to rename a column. Use after confirming the correct IDs. |
| `BUGHERD_UPDATE_PROJECT` | Update Project | Update settings for an existing BugHerd project. Use this to modify a project's name, URL, visibility settings, or guest permissions. Prerequisites: You need a valid project_id. Use list_projects to find existing project IDs, or create_project to create a new one. Note: Only include fields you want to change - omitted fields will retain their current values. |
| `BUGHERD_UPDATE_TASK` | Update Task | Tool to update a task in a project. Use after confirming the project and task IDs. |
| `BUGHERD_UPLOAD_ATTACHMENT` | Upload Attachment | Tool to upload a new attachment and add it to a specific task. Use when you have binary file content ready and need to attach it to a BugHerd task. |

## Supported Triggers

None listed.

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

The Bugherd MCP server is an implementation of the Model Context Protocol that connects your AI agent to Bugherd. It provides structured and secure access so your agent can perform Bugherd 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

No description provided.

### 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

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates your requests to Composio's API
- COMPOSIO_USER_ID identifies the user for session management
- OPENAI_API_KEY enables access to OpenAI's language models
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

No description provided.
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
```

### 5. Initialize Composio client

What's happening:
- We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
- Creating a Composio instance that will manage our connection to Bugherd tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
```

```typescript
const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
```

### 6. Create a Tool Router session

What's happening:
- We're creating a Tool Router session that gives your agent access to Bugherd 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
- This approach allows the agent to dynamically load and use Bugherd tools as needed
```python
# Create Tool Router session for Bugherd
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['bugherd']
)

url = session.mcp.url
```

```typescript
const session = await composio.create(
    userId as string,
    {
        toolkits: ['bugherd']
    }
);

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "bugherd-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
```

```typescript
const client = new MultiServerMCPClient({
    "bugherd-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Bugherd related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
```

```typescript
let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Bugherd related question or task to the agent.\n");

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Complete Code

```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['bugherd']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "bugherd-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Bugherd related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

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

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['bugherd']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "bugherd-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Bugherd related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Conclusion

You've successfully built a LangChain agent that can interact with Bugherd through Composio's Tool Router.
Key features of this implementation:
- Dynamic tool loading through Composio's Tool Router
- Conversation history maintenance for context-aware responses
- Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

## How to build Bugherd MCP Agent with another framework

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

## Related Toolkits

- [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly.
- [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place.
- [Airtable](https://composio.dev/toolkits/airtable) - Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations.
- [Asana](https://composio.dev/toolkits/asana) - Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals.
- [Google Tasks](https://composio.dev/toolkits/googletasks) - Google Tasks is a to-do list and task management tool integrated into Gmail and Google Calendar. It helps you organize, track, and complete tasks across your Google ecosystem.
- [Linear](https://composio.dev/toolkits/linear) - Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity.
- [Jira](https://composio.dev/toolkits/jira) - Jira is Atlassian’s platform for bug tracking, issue tracking, and agile project management. It helps teams organize work, prioritize tasks, and deliver projects efficiently.
- [Clickup](https://composio.dev/toolkits/clickup) - ClickUp is an all-in-one productivity platform for managing tasks, docs, goals, and team collaboration. It streamlines project workflows so teams can work smarter and stay organized in one place.
- [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time.
- [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time.
- [Agiled](https://composio.dev/toolkits/agiled) - Agiled is an all-in-one business management platform for CRM, projects, and finance. It helps you streamline workflows, consolidate client data, and manage business processes in one place.
- [Ascora](https://composio.dev/toolkits/ascora) - Ascora is a cloud-based field service management platform for service businesses. It streamlines scheduling, invoicing, and customer operations in one place.
- [Basecamp](https://composio.dev/toolkits/basecamp) - Basecamp is a project management and team collaboration tool by 37signals. It helps teams organize tasks, share files, and communicate efficiently in one place.
- [Beeminder](https://composio.dev/toolkits/beeminder) - Beeminder is an online goal-tracking platform that uses monetary pledges to keep you motivated. Stay accountable and hit your targets with real financial incentives.
- [Boxhero](https://composio.dev/toolkits/boxhero) - Boxhero is a cloud-based inventory management platform for SMBs, offering real-time updates, barcode scanning, and team collaboration. It helps businesses streamline stock tracking and analytics for smarter inventory decisions.
- [Breathe HR](https://composio.dev/toolkits/breathehr) - Breathe HR is cloud-based HR software for SMEs to manage employee data, absences, and performance. It simplifies HR admin, making it easy to keep employee records accurate and up to date.
- [Breeze](https://composio.dev/toolkits/breeze) - Breeze is a project management platform designed to help teams plan, track, and collaborate on projects. It streamlines workflows and keeps everyone on the same page.
- [Canny](https://composio.dev/toolkits/canny) - Canny is a platform for managing customer feedback and feature requests. It helps teams prioritize product decisions based on real user insights.
- [Chmeetings](https://composio.dev/toolkits/chmeetings) - Chmeetings is a church management platform for events, members, donations, and volunteers. It streamlines church operations and improves community engagement.
- [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management.

## Frequently Asked Questions

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

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

### Can I use Tool Router MCP with LangChain?

Yes, you can. LangChain 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 Bugherd tools.

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

Yes, absolutely. You can configure which Bugherd 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 Bugherd data and credentials are handled as safely as possible.

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