How to Use the Gerrit MCP in LlamaIndex

Q: How do I load Gerrit data into LlamaIndex?

Install llama-index-tools-mcp. Set up a BasicMCPClient and pass the tool spec directly to your FunctionAgent.

Q: Can LlamaIndex search through Gerrit patch sets?

Yes. It uses listpatchsets to pull the revision data and indexes it for semantic search.

Q: Why use LlamaIndex for Gerrit code reviews?

Because standard search sucks. Indexing the output of getproject lets you ask natural language questions about your repository architecture.

Index Gerrit code reviews into LlamaIndex vector stores and run semantic searches on your patch sets.

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Connect Gerrit MCP to LlamaIndex

Create your Vinkius account to connect Gerrit 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 Gerrit with LlamaIndex

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Index Gerrit directly with this MCP Server

Stop searching for old pull requests by hand. You hook this MCP toolset into LlamaIndex, and it pulls your review history into a vector store. The agent runs `query_changes` to grab every open and closed merge in the system. Then it indexes the results. Now you ask your agent why a specific feature was delayed last month. It checks the embedded data from `get_change` and gives you an answer grounded in reality, not a hallucination.

Build a searchable project map

Repository structures get messy fast. Your RAG setup runs `list_projects` and `list_branches` to build a live map of your codebase. It stores the metadata permanently. When a developer asks where a specific release branch lives, the agent knows. It queries the index. You get instant answers based on the actual API state.

Track reviewers and groups

Finding the right person to approve a merge takes too long. LlamaIndex pulls data using `list_groups` and `list_reviewers`. It embeds who owns what. Next time you need a security review, you just ask. The agent searches its knowledge base, finds the exact group ID, and tells you who to ping. You skip the manual lookup entirely.

Setup guide

Set up Gerrit 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 Gerrit 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 Gerrit tools.",
)
response = await agent.run("List recent Gerrit data")

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Common questions about Gerrit MCP in LlamaIndex

Install `llama-index-tools-mcp`. Set up a `BasicMCPClient` and pass the tool spec directly to your `FunctionAgent`.

Yes. It uses `list_patchsets` to pull the revision data and indexes it for semantic search.

You can restrict the agent easily. If you only want it running `query_changes` and `get_change`, just pass those specific tools to the filter.

Because standard search sucks. Indexing the output of `get_project` lets you ask natural language questions about your repository architecture.

The connection drops immediately after the query finishes. The server handles sensitive reviewer emails and internal group descriptions via a strict zero-trust sandbox.

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