Vinkius
Logstash

Logstash MCP for AI. Diagnose pipelines from plain conversation.

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

Logstash (Server-side Log Pipeline API) MCP on Cursor AI Code EditorLogstash (Server-side Log Pipeline API) MCP on Claude Desktop AppLogstash (Server-side Log Pipeline API) MCP on OpenAI Agents SDKLogstash (Server-side Log Pipeline API) MCP on Visual Studio CodeLogstash (Server-side Log Pipeline API) MCP on GitHub Copilot AI AgentLogstash (Server-side Log Pipeline API) MCP on Google Gemini AILogstash (Server-side Log Pipeline API) MCP on Lovable AI DevelopmentLogstash (Server-side Log Pipeline API) MCP on Mistral AI AgentsLogstash (Server-side Log Pipeline API) MCP on Amazon AWS Bedrock

How this MCP server connects to your AI agent

Logstash (Server-side Log Pipeline API) lets you monitor and troubleshoot your data pipelines directly through conversation. Check node health, inspect JVM usage, find performance bottlenecks using `get_hot_threads`, or audit plugin versions—all without running manual commands on port 9600.

It's deep system diagnostics for your AI agent.

What AI agents can do with Logstash (Server-side Log Pipeline API) Automation

Get health report

Retrieves a quick, overall status report for the entire Logstash pipeline instance.

Get hot threads

Analyzes and returns information about threads currently consuming high CPU resources in the process.

Get node info

Gathers general operational details, including OS environment and basic configuration settings for the Logstash node.

+ 3 more capabilities included
Check Pipeline Status

Runs get_health_report to get an immediate status report on the entire Logstash node and its pipelines.

Analyze Performance Metrics

Gathers detailed statistics using get_node_stats, showing JVM heap consumption, event throughput rates, and failure counts.

Diagnose Bottlenecks

Uses get_hot_threads to analyze the current process threads and identify which component is consuming excessive CPU or causing slowdowns.

Audit Configuration

Retrieves deep system details with get_node_info, providing OS environment and general node configuration settings.

Verify Plugins

Runs get_plugins_info to list every installed plugin and confirm its version across the entire Logstash instance.

Included with Plan

Waiting for input…

AI Agent

What AI agents can do with Logstash (Server-side Log Pipeline API): 6 Tools for Diagnostics

Analyze your Logstash stack using these six tools. Monitor health status, inspect performance metrics, and audit plugin versions directly through an AI agent.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using Logstash (Server-side Log Pipeline API) on Vinkius

Get Health Report

Retrieves a quick, overall status report for the entire Logstash pipeline instance.

Get Hot Threads

Analyzes and returns information about threads currently consuming high CPU...

Get Node Info

Gathers general operational details, including OS environment and basic...

Get Plugins Info

Lists all installed plugins and their associated versions within the Logstash...

Get Root

Returns general root information about the underlying system where Logstash is...

Get Node Stats

Retrieves detailed metrics on JVM memory usage (heap) and event processing rates per second.

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Claude AI

1

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

2

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

3

Start a conversation

Open a new chat. The Logstash integration is available immediately — no restart needed.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

  • Import from OpenAPI, Swagger, or YAML specs
  • Create Agent Skills with progressive disclosure
  • Deploy to edge with MCPFusion framework
  • Built in DLP, auth, and compliance on every call
  • Real time usage dashboard and cost metering
  • Publish to catalog or keep private
Start building

Make Your AI Do More

Start with Logstash (Server-side Log Pipeline API), then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

  • Use this MCP plus 5,100+ others, all in one place
  • Add new capabilities to your AI anytime you want
  • Every connection is secured and compliant automatically
  • Track usage and costs across all your servers
  • Works with Claude, ChatGPT, Cursor, and more
  • New servers added to the catalog every week
Logstash MCP server cover

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Logstash. 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.

VINKIUS INFRASTRUCTURE

Cloud Hosted

Managed infra

V8 Isolated

Sandboxed per request

Zero-Trust Proxy

No stored credentials

DLP Enforced

Policy on every call

GDPR Compliant

EU data residency

Token Compression

~60% cost reduction

Your data is protected. See how we built it.

Built on the Model Context Protocol (MCP) for Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 6 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Juggling dashboards and terminals for log diagnostics is a nightmare., Solved with Vinkius AI Gateway

Today, diagnosing an issue means jumping between three different places. You check Kibana to see if logs are coming in; you run `curl` on the terminal to check API status; and then you switch over to a dedicated metrics dashboard just to guess where the bottleneck is. It’s slow, it's manual, and you're always one step behind.

With this MCP server, your AI client does all that work for you. You ask it—'Why are my logs slowing down?'—and it runs a series of checks (`get_health_report`, `get_node_stats`, etc.) internally. It then gives you one clean answer in the chat: 'The issue is X, caused by Y.' That’s what you actually get.

Logstash MCP Server: Get deep visibility into pipeline health.

Before, if performance dipped, you'd have to stop and remember which metrics mattered—was it JVM heap? Was it event rate? Did a new plugin break something? You had to run multiple commands just to build a picture.

Now, your agent coordinates these checks. It knows exactly when to use `get_node_stats` for numbers or `get_hot_threads` for culprits. This capability means you don't have to think like an SRE; the tool stack handles the complexity so you can just focus on fixing the problem.

What your AI can actually do with this

You shouldn't have to run manual curl commands or mess around with port 9600 just to see if your data pipeline is actually working. This server lets you talk to your Logstash instance, letting your agent act like a dedicated SRE who keeps an eye on everything. Forget the console; you just ask it, and it gives you the diagnostics.

Checking Pipeline Status: To get an immediate read on how things are running, you'll use get_health_report. This spits out a quick, overall status report for the entire Logstash node and all its connected pipelines. It tells you right away if everything's green or if you need to worry about it.

Analyzing Performance Metrics: Need deep stats? You run get_node_stats when you wanna see detailed metrics on the JVM heap usage, plus how many events are actually getting processed per second. This tool gives you a full picture of your throughput rates and failure counts. When you're digging into system details, get_node_info pulls general operational data—you get the OS environment info and basic node configuration settings.

For even deeper root access, get_root returns general root information about the actual underlying system where Logstash is running.

Diagnosing Bottlenecks: If your pipeline slows down or you suspect a CPU spike, you run get_hot_threads. This analyzes the current process threads and tells you exactly which component's hogging the high CPU resources. When you wanna verify what plugins are installed across the board, you use get_plugins_info, which lists every single plugin name and confirms its version across the entire Logstash environment.

Built · Hosted · Managed by Vinkius Logstash MCP Server - Monitor Pipeline Health & Stats
Server ID 019e5d30-5446-73a1-973d-5d6147555423
Vinkius Inspector
Compliance Grade A+
Score 100/100
Vinkius Inspector Badge — Score 100/100

Questions you might have

How do I check if my Logstash node is healthy using get_health_report? +

You simply ask your agent to run get_health_report. It provides an immediate status, telling you in plain English if the overall pipeline status is green (good), yellow (warning), or red (down).

What's the difference between get_node_stats and getting node info? +

get_node_stats gives you live, numerical metrics like JVM heap usage and event processing rates. get_node_info, by contrast, provides static details about the OS environment and general configuration settings.

Should I use get_hot_threads every time there's a slowdown? +

Yes, when you suspect performance issues or CPU spikes, get_hot_threads is your best bet. It tells you exactly which thread—and thus which part of the pipeline—is consuming too much processing power.

What if I forget to check plugin versions? Is it safe? +

No, relying only on overall health isn't enough. Always run get_plugins_info before major changes to verify that all installed plugins match the documented version and are consistent across environments.

What credentials or connection details must I provide when using any tool like get_node_stats? +

You need a valid Logstash API URL and the necessary authentication tokens. The AI agent handles passing these credentials securely to the server endpoint, so you just need to ensure your client has access to them.

Using get_node_info, what specific configuration details can I pull for a pipeline? +

You retrieve the active configuration files and environmental settings used by the node. This lets you verify if changes were made outside of the standard deployment process.

If I use get_node_stats, how do I interpret JVM heap usage versus event flow rate? +

JVM heap usage tracks memory consumption; event flow rate measures throughput (events per second). High heap usage paired with low throughput suggests a potential memory leak or bottleneck.

How does get_root help me diagnose environmental drift across different Logstash deployments? +

It provides baseline system information, including the OS version and environment variables. This allows you to compare environments quickly to find unexpected differences that affect performance.

How can I check if my Logstash pipelines are running correctly? +

You can use the get_health_report tool. It returns a status report (green, yellow, or red) for the Logstash instance and its active pipelines, allowing you to quickly identify any operational issues.

Can the AI help me find performance bottlenecks in my Logstash node? +

Yes! Use get_node_stats to see detailed JVM and event metrics, or get_hot_threads to see which parts of the process are consuming the most CPU. This helps pinpoint exactly where the slowdown is occurring.

Is it possible to list all installed plugins and their versions? +

Absolutely. The get_plugins_info tool retrieves a complete list of all currently installed Logstash plugins along with their specific version numbers.

Built & Managed by Vinkius 30s setup 6 tools

We've already built the connector for Logstash. Just plug in your AI agents and start using Vinkius.

No hosting. No infrastructure. No complex setup.
All 6 tools are live and waiting. You're up and running in seconds.

Vinkius runs on Claude Claude
Vinkius runs on ChatGPT ChatGPT
Vinkius runs on Cursor Cursor
Vinkius runs on Gemini Gemini
Vinkius runs on Windsurf Windsurf
Vinkius runs on VS Code VS Code
Vinkius runs on JetBrains JetBrains
Vinkius runs on Vercel Vercel
+ other MCP clients

Vinkius gives your AI agents access to the full catalog of app connectors, all fully managed, secure, and enterprise-ready. One subscription, every tool you need.

Zero hosting required Full MCP catalog included Enterprise-grade security Auto-updated by Vinkius

Built, hosted, and secured by Vinkius. You just connect and go.