Logstash MCP. Diagnose pipelines from plain conversation.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
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 your AI agents can do
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.
Runs get_health_report to get an immediate status report on the entire Logstash node and its pipelines.
Gathers detailed statistics using get_node_stats, showing JVM heap consumption, event throughput rates, and failure counts.
Uses get_hot_threads to analyze the current process threads and identify which component is consuming excessive CPU or causing slowdowns.
Retrieves deep system details with get_node_info, providing OS environment and general node configuration settings.
Runs get_plugins_info to list every installed plugin and confirm its version across the entire Logstash instance.
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Supported MCP Clients
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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.
019e5d30get health report
Retrieves a quick, overall status report for the entire Logstash pipeline instance.
019e5d30get hot threads
Analyzes and returns information about threads currently consuming high CPU resources in the process.
019e5d30get node info
Gathers general operational details, including OS environment and basic configuration settings for the Logstash node.
019e5d30get node stats
Retrieves detailed metrics on JVM memory usage (heap) and event processing rates per second.
019e5d30get plugins info
Lists all installed plugins and their associated versions within the Logstash environment.
019e5d30get root
Returns general root information about the underlying system where Logstash is running.
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
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- Publish to catalog or keep private
Make Your AI Do More
Start with Logstash (Server-side Log Pipeline API), then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ 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
What you can do with this MCP connector
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.
How Logstash MCP Works
- 1 Subscribe to this server on Vinkius, providing your specific Logstash API URL and credentials.
- 2 Direct your AI client (Claude, Cursor, etc.) to the MCP Server endpoint. The agent will now have access to all diagnostic tools.
- 3 Prompt your agent with a natural language query (e.g., 'Check node health' or 'Show me hot threads') and get an immediate, structured response.
The bottom line is: you talk to your AI client like a teammate, and it runs the necessary diagnostic code on your Logstash instance for you.
Who Is Logstash MCP For?
Site Reliability Engineers (SREs) who get paid to fix things at 2 am. DevOps staff tired of juggling SSH sessions and web dashboards. Data Engineers responsible for making sure petabytes of logs are processed correctly every minute.
Runs get_hot_threads immediately when alerts fire, pinpointing the exact plugin or function causing CPU spikes without running manual diagnostic scripts.
Checks compliance by calling get_plugins_info across different environments to ensure version parity before a major deployment.
Validates the stability of data ingestion pipelines using get_node_stats, confirming event throughput and JVM memory usage are within acceptable ranges.
What Changes When You Connect
- See real-time performance metrics. Instead of digging through Grafana dashboards, run
get_node_statsand instantly get JVM heap usage and event throughput rates for the node. - Pinpoint bottlenecks fast. If latency spikes, use
get_hot_threads. It tells you which specific thread or plugin is spiking the CPU, so you don't waste time guessing. - Audit configuration easily. Need to check if all environments are running the same version of a filter plugin? Just run
get_plugins_infoand compare the output. - Get immediate status checks. Don't know if things are green or red? A single prompt calling
get_health_reportgives you the overall status instantly. - Save time on manual commands. You skip running
curl localhost:9600/.... Your AI client handles the API calls, passing structured data back to your chat window.
Real-World Use Cases
Investigating a Sudden Slowdown
A Data Engineer notices that log ingestion has slowed down. They prompt their agent: 'What's wrong with the pipeline?' The agent first runs get_health_report (to confirm status) and then calls get_hot_threads. The resulting data shows a specific output plugin is hogging 90% CPU, solving the mystery in minutes.
Pre-Deployment Compliance Check
A DevOps team member needs to ensure all staging nodes have the correct version of their custom geo-ip filter. They run get_plugins_info on three different nodes, verifying that every single node reports the required plugin version before deployment.
Memory Leak Investigation
An SRE suspects a gradual memory leak. They prompt for system statistics, triggering get_node_stats. The output shows steadily increasing JVM heap usage over time, confirming the suspected memory pressure and narrowing down the source.
Initial System Handshake
A new team member joins and needs to know what's running. They ask their agent for general system context, triggering get_node_info and get_root, giving them a full picture of the operating environment without needing access to multiple shell tabs.
The Tradeoffs
Only checking one metric.
A user only runs get_node_stats and sees low heap usage. They assume everything is fine, missing a performance issue that isn't memory-related.
→
Always start with a holistic view. Run get_health_report first for baseline status. If the report shows yellow/red flags, then use targeted tools like get_hot_threads to find the actual root cause.
Manually calling APIs.
The user opens their terminal and runs a multi-step curl sequence across several ports just to gather stats, which is slow and error-prone.
→
Let your agent handle it. Just ask the AI client: 'Check node performance metrics.' The agent manages the necessary calls (like get_node_stats) in the background and delivers one clean answer.
Ignoring plugin versions.
A team member assumes that because the overall health is green, all components are compatible, only to find an unexpected bug caused by a mismatched dependency version.
→
Before trusting any 'green' status, run get_plugins_info. Verify every single plugin version against your documented standard. Mismatching versions kill pipelines.
When It Fits, When It Doesn't
Use this server if your primary need is deep, systematic diagnostics of a complex data pipeline (e.g., diagnosing intermittent slowdowns or memory leaks). You're looking for why something broke, not just if it broke.
Don't use it if:
1. You only need simple uptime status (use your existing dashboarding tool like Grafana).
2. Your logs are in a different system (like Kafka or S3); this is Logstash-specific.
3. You just want to see the raw data stream; you'll still need Kibana for that.
When in doubt, run get_health_report first. If that passes, but performance feels off, immediately move to get_hot_threads and then back to get_node_stats. This sequence minimizes overhead while covering the most common failure vectors.
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.
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Works with 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 server provides 6 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Juggling dashboards and terminals for log diagnostics is a nightmare.
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.
Common Questions About Logstash MCP
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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