How to Use the Airbrake MCP in LlamaIndex

Q: How do I use LlamaIndex to find similar errors in Airbrake?

As your agent indexes error data from the listnotices tool, it builds a vector store. You can then query that store to find errors that are semantically similar, even if the text isn't an exact match.

Q: Can I build a LlamaIndex RAG app over my Airbrake project settings?

Definitely. Have your agent call listprojects and getproject, then index the resulting configuration data. This creates a knowledge base your RAG application can use to answer questions about your setup.

Q: What happens when I list deployments with this MCP Server?

The listdeploys tool returns data on recent deployments. Your LlamaIndex agent can index this information to build a timeline, letting you correlate error spikes with specific code changes.

Q: Is my Airbrake project configuration data secure when using LlamaIndex?

It is. When your agent calls getproject, the data passes through a Vinkius-managed sandbox that's discarded after the request. The connection is secured by your single endpoint token, so there are no credentials to manage.

Turn your Airbrake error history into a queryable knowledge base with LlamaIndex.

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Connect Airbrake MCP to LlamaIndex

Create your Vinkius account to connect Airbrake 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 Airbrake with LlamaIndex

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Index and Query Your Error History

Stop digging through logs. Have your agent periodically use `list_error_groups` and `list_notices` to pull down the latest error data. LlamaIndex doesn't just see this data—it indexes it. Now it's part of a searchable knowledge base. You can ask your agent, "show me all timeout errors from the last week," and it will query its own index to give you a precise answer, grounded in the actual data it pulled from Airbrake.

Enrich New Errors with Historical Context

Your agent can be more than a simple reporter. Before it uses `report_notice` to create a new issue, it can first perform a vector search on its own knowledge base. It looks for similar errors it has indexed in the past. If it finds a match, it can add useful context to the new report. Think things like, "This looks like the bug from last month that Sarah fixed," or it could attach the previous solution. This makes your error reporting smarter.

Build a RAG Agent for Your Airbrake Setup

Your project configurations are data, too. Use this MCP Server to run `list_projects` and `get_project` for all your apps, then have LlamaIndex index the results. You've just built a RAG agent for your own infrastructure. Now you can ask it direct questions. "What's the project ID for WebApp-Production?" or "Which projects are still on the free tier?" You get instant, accurate answers without having to click around in a UI.

Setup guide

Set up Airbrake 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 Airbrake 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 Airbrake tools.",
)
response = await agent.run("List recent Airbrake 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 Airbrake. 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 Airbrake MCP in LlamaIndex

Yes. LlamaIndex is built to index the output of tool calls. Your agent fetches errors with `list_notices`, and those results become a searchable knowledge base you can query with natural language.

As your agent indexes error data from the `list_notices` tool, it builds a vector store. You can then query that store to find errors that are semantically similar, even if the text isn't an exact match.

Definitely. Have your agent call `list_projects` and `get_project`, then index the resulting configuration data. This creates a knowledge base your RAG application can use to answer questions about your setup.

The `list_deploys` tool returns data on recent deployments. Your LlamaIndex agent can index this information to build a timeline, letting you correlate error spikes with specific code changes.

It is. When your agent calls `get_project`, the data passes through a Vinkius-managed sandbox that's discarded after the request. The connection is secured by your single endpoint token, so there are no credentials to manage.

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