Sentry MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Sentry as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
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 Sentry. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Sentry?"
)
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 Sentry MCP Server
Equip your favorite LLM interface with direct, real-time investigative access over your application's Sentry operational environments. Skip the grueling task of combing through the rigid crash dashboard visually. Now, your AI can pull up the latest software exceptions directly into Cursor or an MCP-enabled chat window, read the contextual stack trace natively, and even close out resolved bugs.
LlamaIndex agents combine Sentry tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the 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
- Live Crash Monitoring — Query the
list_issuesfunctionality at any time to instantly see which endpoints or functions are currently malfunctioning and throwing fatal alerts - Deep Error Inspection — Feed an
issue_idto the agent viaget_issue_details. The LLM will devour the entire stack trace, evaluate the environmental metadata, and suggest precisely which lines of code need attention - Project & Organization Forensics — Interrogate the AI regarding internal structures (
list_users,list_teams) and easily scan separate software branches or repositories (list_projects) configured in your Sentry silo - Alert Triage (Mutable) — Dictate the agent to close resolved items (
resolve_issue), marking the exception safely as handled without having to load the web interface
The Sentry 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.
How to Connect Sentry to LlamaIndex via MCP
Follow these steps to integrate the Sentry MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Sentry
Why Use LlamaIndex with the Sentry MCP Server
LlamaIndex provides unique advantages when paired with Sentry through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Sentry tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Sentry tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Sentry, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Sentry tools were called, what data was returned, and how it influenced the final answer
Sentry + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Sentry MCP Server delivers measurable value.
Hybrid search: combine Sentry real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Sentry 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 Sentry for fresh data
Analytical workflows: chain Sentry queries with LlamaIndex's data connectors to build multi-source analytical reports
Sentry MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Sentry to LlamaIndex via MCP:
delete_issue
This action is irreversible. Permanently deletes an issue
get_event_details
Retrieves details for a specific event
get_issue_details
Retrieves details for a specific issue
list_events
Lists recent events for a project
list_issues
Lists all issues (errors) in a project
list_organization_teams
Lists all teams in an organization
list_organization_users
Lists all users in an organization
list_organizations
Lists all Sentry organizations
list_projects
Lists all projects in an organization
resolve_issue
This is a reversible side-effect. Resolves an issue in Sentry
Example Prompts for Sentry in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Sentry immediately.
"Enumerate the most recently flared active open errors across the 'frontend-ui' project portal in Sentry."
"Fetch all pertinent internal parameters regarding issue id 6B3VX4921."
"I've deployed a patch fixing the deadlock in db.ts. Mutate this specific issue globally to 'resolved'."
Troubleshooting Sentry MCP Server with LlamaIndex
Common issues when connecting Sentry to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpSentry + LlamaIndex FAQ
Common questions about integrating Sentry MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Sentry with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Sentry to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.