How to integrate Bitbucket MCP with Mastra AI

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Introduction

This guide walks you through connecting Bitbucket to Mastra AI using the Composio tool router. By the end, you'll have a working Bitbucket agent that can create a new branch off main, open a pull request for my feature, comment on the latest open issue, fetch readme file from repository through natural language commands.

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

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

TL;DR

Here's what you'll learn:
  • Set up your environment so Mastra, OpenAI, and Composio work together
  • Create a Tool Router session in Composio that exposes Bitbucket tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch Bitbucket tool definitions and attach them as a toolset
  • Build a Mastra agent that can reason, call tools, and return structured results
  • Run an interactive CLI where you can chat with your Bitbucket agent

What is Mastra AI?

Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP.

Key features include:

  • MCP Client: Built-in support for Model Context Protocol servers
  • Toolsets: Organize tools into logical groups
  • Step Callbacks: Monitor and debug agent execution
  • OpenAI Integration: Works with OpenAI models via @ai-sdk/openai

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

The Bitbucket MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Bitbucket account. It provides structured and secure access to your repositories, issues, and pull requests, so your agent can create branches, manage issues, review code, and handle repository operations for you.

  • Branch and repository management: Let your agent create new branches for feature work or initialize fresh repositories within your Bitbucket workspace—no manual setup required.
  • Automated issue tracking: Have your agent create, comment on, or delete issues to streamline team collaboration and bug tracking directly from your workflows.
  • Pull request automation: Empower your agent to open new pull requests for code review, ensuring changes are properly tracked and integrated.
  • File and snippet operations: Ask your agent to fetch specific files from any branch or commit, or to post comments on code snippets for contextual discussions.
  • User profile and workspace insights: Retrieve your Bitbucket user profile details on demand, making it easy to personalize and audit agent-driven actions.

Supported Tools & Triggers

Tools
Create a branchCreates a new branch in a bitbucket repository from a target commit hash; the branch name must be unique, adhere to bitbucket's naming conventions, and not include the 'refs/heads/' prefix.
Create an issueCreates a new issue in a bitbucket repository, setting the authenticated user as reporter; ensures assignee (if provided) has repository access, and that any specified milestone, version, or component ids exist.
Create an issue commentAdds a new comment with markdown support to an existing bitbucket issue.
Create a pull requestCreates a new pull request in a specified bitbucket repository, ensuring the source branch exists and is distinct from the (optional) destination branch.
Create repositoryCreates a new bitbucket 'git' repository in a specified workspace, defaulting to the workspace's oldest project if `project key` is not provided.
Create snippet commentPosts a new top-level comment or a threaded reply to an existing comment on a specified bitbucket snippet.
Delete issuePermanently deletes a specific issue, identified by its `issue id`, from the repository specified by `repo slug` within the given `workspace`.
Delete repositoryPermanently deletes a specified bitbucket repository; this action is irreversible and does not affect forks.
Get current userRetrieves the profile information (uuid, display name, links, creation date) for the currently authenticated bitbucket user.
Get file from repositoryRetrieves a specific file's content from a bitbucket repository at a given commit (hash, branch, or tag), failing if the file path is invalid for that commit.
Get Pull RequestGet a single pull request by id with complete details.
Get snippetRetrieves a specific bitbucket snippet by its encoded id from an existing workspace, returning its metadata and file structure.
List pull requestsLists pull requests in a specified, accessible bitbucket repository, optionally filtering by state (open, merged, declined).
List repositories in workspaceLists repositories in a specified bitbucket workspace, accessible to the authenticated user, with options to filter by role or query string, and sort results.
List workspace membersLists all members of a specified bitbucket workspace; the workspace must exist.
List workspacesLists bitbucket workspaces accessible to the authenticated user, optionally filtered and sorted.
Update an issueUpdates an existing issue in a bitbucket repository by modifying specified attributes; requires `workspace`, `repo slug`, `issue id`, and at least one attribute to update.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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

Prerequisites

Before starting, make sure you have:
  • Node.js 18 or higher
  • A Composio account with an active API key
  • An OpenAI API key
  • Basic familiarity with TypeScript

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key.
  • You need credits or a connected billing setup to use the models.
  • Store the key somewhere safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Go to Settings and copy your API key.
  • This key lets your Mastra agent talk to Composio and reach Bitbucket through MCP.

Install dependencies

bash
npm install @composio/core @mastra/core @mastra/mcp @ai-sdk/openai dotenv

Install the required packages.

What's happening:

  • @composio/core is the Composio SDK for creating MCP sessions
  • @mastra/core provides the Agent class
  • @mastra/mcp is Mastra's MCP client
  • @ai-sdk/openai is the model wrapper for OpenAI
  • dotenv loads environment variables from .env

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio
  • COMPOSIO_USER_ID tells Composio which user this session belongs to
  • OPENAI_API_KEY lets the Mastra agent call OpenAI models

Import libraries and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey as string,
});
What's happening:
  • dotenv/config auto loads your .env so process.env.* is available
  • openai gives you a Mastra compatible model wrapper
  • Agent is the Mastra agent that will call tools and produce answers
  • MCPClient connects Mastra to your Composio MCP server
  • Composio is used to create a Tool Router session

Create a Tool Router session for Bitbucket

typescript
async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["bitbucket"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("Bitbucket MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "bitbucket" for Bitbucket access
  • session.mcp.url is the MCP URL that Mastra's MCPClient will connect to

Configure Mastra MCP client and fetch tools

typescript
const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      nasdaq: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

console.log("Fetching MCP tools from Composio...");
const composioTools = await mcpClient.getTools();
console.log("Number of tools:", Object.keys(composioTools).length);
What's happening:
  • MCPClient takes an id for this client and a list of MCP servers
  • The headers property includes the x-api-key for authentication
  • getTools fetches the tool definitions exposed by the Bitbucket toolkit

Create the Mastra agent

typescript
const agent = new Agent({
    name: "bitbucket-mastra-agent",
    instructions: "You are an AI agent with Bitbucket tools via Composio.",
    model: "openai/gpt-5",
  });
What's happening:
  • Agent is the core Mastra agent
  • name is just an identifier for logging and debugging
  • instructions guide the agent to use tools instead of only answering in natural language
  • model uses openai("gpt-5") to configure the underlying LLM

Set up interactive chat interface

typescript
let messages: AiMessageType[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end.\n");

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

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;
  }

  messages.push({
    id: crypto.randomUUID(),
    role: "user",
    content: trimmedInput,
  });

  console.log("\nAgent is thinking...\n");

  try {
    const response = await agent.generate(messages, {
      toolsets: {
        bitbucket: composioTools,
      },
      maxSteps: 8,
    });

    const { text } = response;

    if (text && text.trim().length > 0) {
      console.log(`Agent: ${text}\n`);
        messages.push({
          id: crypto.randomUUID(),
          role: "assistant",
          content: text,
        });
      }
    } catch (error) {
      console.error("\nError:", error);
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    console.log("\nSession ended.");
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • messages keeps the full conversation history in Mastra's expected format
  • agent.generate runs the agent with conversation history and Bitbucket toolsets
  • maxSteps limits how many tool calls the agent can take in a single run
  • onStepFinish is a hook that prints intermediate steps for debugging

Complete Code

Here's the complete code to get you started with Bitbucket and Mastra AI:

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({ apiKey: composioAPIKey as string });

async function main() {
  const session = await composio.create(composioUserID as string, {
    toolkits: ["bitbucket"],
  });

  const composioMCPUrl = session.mcp.url;

  const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      bitbucket: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "bitbucket-mastra-agent",
    instructions: "You are an AI agent with Bitbucket tools via Composio.",
    model: "openai/gpt-5",
  });

  let messages: AiMessageType[] = [];

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

  rl.prompt();

  rl.on("line", async (input: string) => {
    const trimmed = input.trim();
    if (["exit", "quit"].includes(trimmed.toLowerCase())) {
      rl.close();
      return;
    }

    messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed });

    const { text } = await agent.generate(messages, {
      toolsets: { bitbucket: composioTools },
      maxSteps: 8,
    });

    if (text) {
      console.log(`Agent: ${text}\n`);
      messages.push({ id: crypto.randomUUID(), role: "assistant", content: text });
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main();

Conclusion

You've built a Mastra AI agent that can interact with Bitbucket through Composio's Tool Router. You can extend this further by:
  • Adding other toolkits like Gmail, Slack, or GitHub
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows

How to build Bitbucket MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Bitbucket MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Bitbucket MCP with Claude Agent SDK

Google ADK

Google ADK

Use Bitbucket MCP with Google ADK

LangChain

LangChain

Use Bitbucket MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Bitbucket MCP with Vercel AI SDK

LlamaIndex

LlamaIndex

Use Bitbucket MCP with LlamaIndex

CrewAI

CrewAI

Use Bitbucket MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Bitbucket MCP?

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

Can I use Tool Router MCP with Mastra AI?

Yes, you can. Mastra 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 Bitbucket tools.

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

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

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