How to Use the Bitbucket MCP in LlamaIndex

Q: How do I index my Bitbucket repository data using LlamaIndex?

You can use the MCP client to load tools like listrepositories directly into your agent. From there, the agent fetches the repository metadata and writes it to your vector store.

Q: Can I query Bitbucket pipelines semantically with LlamaIndex?

Yes, you can index pipeline runs using listpipelines. This lets you query build history semantically to find patterns in failing builds.

Q: How do I filter which repositories are indexed?

You can pass specific parameters to listrepositories or filter the tool output before passing it to your index. This ensures only relevant codebases are scanned.

Index your Bitbucket repositories directly into your LlamaIndex knowledge graphs using this MCP Server.

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Connect Bitbucket MCP to LlamaIndex

Create your Vinkius account to connect Bitbucket to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Bitbucket with LlamaIndex

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Build RAG pipelines over Bitbucket commit history

We connect `list_commits` and `get_repository` to pull commit logs directly into your LlamaIndex vector store. Your agent indexes the commit messages and code changes, turning raw git history into a searchable knowledge base. Instead of guessing when a bug was introduced, you can query your index semantically. The agent searches the vector store using this MCP integration to find the exact commit that modified the target file.

Semantic issue querying via LlamaIndex MCP Server

This feature uses `list_issues` and `get_pull_request` to link user-reported bugs with active development branches. Your agent indexes open issues and pull requests, establishing semantic relationships between code fixes and reported problems. This creates a unified query interface for your project. You can ask your agent about the status of a bug, and it will pull the relevant PR details without manual search.

Workspace metadata indexing

By pulling data from `list_workspaces` and `list_repositories`, your agent maps your entire development footprint. It indexes the structure of your workspaces, making it easy to query which repositories belong to which teams. This metadata becomes part of your agent's context. When you ask where a specific service is hosted, the agent queries the indexed workspace layout to give you the exact repository path.

Setup guide

Set up Bitbucket MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Bitbucket MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Bitbucket tools.",
)
response = await agent.run("List recent Bitbucket data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Bitbucket. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Bitbucket MCP in LlamaIndex

You can use the MCP client to load tools like `list_repositories` directly into your agent. From there, the agent fetches the repository metadata and writes it to your vector store.

LlamaIndex handles the caching at the index level. When your agent calls `get_pull_request`, it can store the response in your local vector database to avoid hitting API rate limits.

Yes, you can index pipeline runs using `list_pipelines`. This lets you query build history semantically to find patterns in failing builds.

You can pass specific parameters to `list_repositories` or filter the tool output before passing it to your index. This ensures only relevant codebases are scanned.

Your repository metadata remains entirely under your control. The MCP Server processes the data in a secure Vinkius sandbox, and any indexed vector data is stored in your own local or cloud database.

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