Airbrake MCP Server for LlamaIndexGive LlamaIndex instant access to 10 tools to Check Airbrake Status, Get Error Group, Get Project, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Airbrake as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The Airbrake app connector for LlamaIndex is a standout in the Productivity category — giving your AI agent 10 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Airbrake. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Airbrake?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Airbrake MCP Server
Connect your Airbrake account to any AI agent and manage your entire error monitoring workflow through natural conversation.
LlamaIndex agents combine Airbrake tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Project Management — List all monitored projects and fetch detailed project configuration
- Error Group Analysis — List error groups by project with occurrence counts, severity, and last-seen timestamps
- Deep Error Inspection — Inspect individual error groups with full stack traces, affected users, and frequency data
- Error Notices — List individual error occurrences within a group and report custom errors for tracking
- Deployment Tracking — List all tracked Installments and record new releases with version and environment info
- Environment Overview — View all configured environments (production, staging, development) per project
- Health Check — Verify API connectivity and account status
The Airbrake MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 10 Airbrake tools available for LlamaIndex
When LlamaIndex connects to Airbrake through Vinkius, your AI agent gets direct access to every tool listed below — spanning airbrake, error-monitoring-api, exception-tracking, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Verify API connectivity
Get error group details
Get project details
List deployments
List environments
List error groups
List error notices
List all projects
Report an error notice
Track a deployment
Connect Airbrake to LlamaIndex via MCP
Follow these steps to wire Airbrake into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Airbrake MCP Server
LlamaIndex provides unique advantages when paired with Airbrake through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Airbrake tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Airbrake tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Airbrake, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Airbrake tools were called, what data was returned, and how it influenced the final answer
Airbrake + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Airbrake MCP Server delivers measurable value.
Hybrid search: combine Airbrake real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Airbrake to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Airbrake for fresh data
Analytical workflows: chain Airbrake queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Airbrake in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Airbrake immediately.
"List all my Airbrake projects."
"Show me the error groups for project 120394."
"Record a Installment for project 120394: version 3.2.1, production."
Troubleshooting Airbrake MCP Server with LlamaIndex
Common issues when connecting Airbrake to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAirbrake + LlamaIndex FAQ
Common questions about integrating Airbrake MCP Server with LlamaIndex.