StatHat MCP for AI. Log every metric—from counters to complex values.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
How this MCP server connects to your AI agent
StatHat lets your AI agent post custom metrics and statistics directly to your observability stack. Use StatHat's tools to increment counters, record specific values (like latency), or batch-update multiple stats without leaving your chat client.
It’s built for developers who need real-time performance data logged instantly.
What AI agents can do with StatHat Automation
Post classic counter
Increments a specific metric counter, useful for tracking discrete event occurrences.
Post classic value
Sets or updates a stat with a continuous numerical value, like response time in milliseconds.
Post ez stat
Creates and posts a statistic using an easy name. It handles creating the metric automatically if it doesn't exist.
Use post_classic_counter to increase the count for any specific event key by exactly one.
Send precise, continuous numbers—like latency or resource usage—to a dedicated stat key using post_classic_value.
Invoke post_ez_stat to create and log an entirely new metric simply by providing a descriptive name, no setup required.
Send large sets of diverse metrics (counts, values, timestamps) in one request using post_json_stats for efficiency.
Ask an AI about this
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What AI agents can do with StatHat MCP Server: 4 Tools for Metric & Value Posting
These four tools allow your AI agent to perform every type of data logging job: incrementing simple counts, setting specific values, creating new metrics on the fly, or handling complex batch updates.
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 StatHat on VinkiusPost Classic Counter
Increments a specific metric counter, useful for tracking discrete event occurrences.
Post Classic Value
Sets or updates a stat with a continuous numerical value, like response time in...
Post Ez Stat
Creates and posts a statistic using an easy name. It handles creating the metric...
Post Json Stats
Accepts structured JSON data to post multiple different statistics (counts and...
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
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
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.
Start a conversation
Open a new chat. The StatHat 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
Make Your AI Do More
Start with StatHat, 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
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by StatHat. 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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Managed infra
V8 Isolated
Sandboxed per request
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Policy on every call
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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 4 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Logging key business stats usually requires too many clicks., Solved with Vinkius AI Gateway
Today, if you need to log an event count or update a KPI, your process looks like this: 1. Open dashboard A. 2. Find the correct metric widget. 3. Manually click 'Add Data Point' or copy-paste a value from your terminal output into a spreadsheet cell. This wastes time and introduces friction.
With StatHat, you skip all that. You just tell your agent: 'Increment the 'Completed Orders' count by 1.' Done. Your AI client handles the API call; you just get the confirmation back in chat.
StatHat MCP Server: Log metrics and values instantly.
The biggest manual step that goes away is writing boilerplate SDK code. You don't have to manage API keys, endpoint URLs, or payload structuring just to log a count or value. Your agent handles the syntax.
It’s pure intent mapping. If you can describe it in plain English—like 'Log 120ms latency for checkout'—your agent and StatHat make sure it gets logged accurately.
What your AI can actually do with this
Forget writing boilerplate SDK code or manually updating dashboards just to log a metric. When you connect your AI client to StatHat, you're giving your agent an instant pipeline for performance data. It’s built for developers who need real-time tracking—you don't want to leave the chat window every time something happens, so StatHat lets your agent post custom metrics and stats directly to your observability stack using four specific tools.
When you use this server, your AI client acts as the bridge. Instead of telling your agent what to do with code, you just tell it what happened; then, the right tool handles sending that data instantly. You'll find the functionality covers everything from logging a single event count to dumping massive sets of diverse metrics all at once.
Increment Event Counters: You can use post_classic_counter when you need to track simple, discrete events. If something happens—say, a user clicks a specific button or an API call succeeds—you instruct your agent to run this tool, and it increases the count for that specific event key by exactly one. This is how you measure total occurrences of any defined action.
Record Specific Numerical Values: For continuous numbers, you'll use post_classic_value. Think latency measurements in milliseconds, CPU usage percentages, or resource availability; these are all values. When you call this tool, your agent sends a precise number to a dedicated stat key, allowing you to track rates and magnitudes over time.
Create Metrics on the Fly: Sometimes you don't know if a metric key exists yet—you just need to log it immediately. That’s where post_ez_stat comes in handy. You give this tool a descriptive name, and StatHat automatically handles creating the metric for you, logging that new stat without any setup required on your end.
Batch Log Multiple Statistics: When you're dealing with large amounts of data—maybe counts and values from several different sources at once—you don't want to make multiple API calls. You use post_json_stats. This tool accepts structured JSON data, letting you send diverse metrics (counts, specific numerical values, and timestamps) in a single, efficient call.
It’s how you keep your logging fast when you're tracking everything.
If you need to track the number of times a user hit 'submit,' you invoke post_classic_counter. If you're measuring the average time it took for a database query to run, you use post_classic_value with that continuous time. Need to log a new metric called 'Payment Gateway Failures'? Use post_ez_stat just by giving it the name.
And when your application generates dozens of data points—like counts from three different endpoints and values for two different service latencies—you bundle all that crap into one JSON payload using post_json_stats. This server gives you granular, immediate control over metrics logging without forcing you to write a single line of extra code.
019e5d59-ea2c-72e3-93ac-85bf0ba0440f 
Here's how it actually works
The bottom line is: you use your AI agent to talk to StatHat, and StatHat handles the data logging details for you.
Subscribe to the StatHat server and provide your EZ or User Key.
Tell your AI agent what metrics need logging (e.g., 'Increment user signups by 1').
The agent calls the appropriate tool (post_classic_counter, post_ez_stat, etc.), and StatHat confirms the metric update.
Who is this actually for?
This server is for engineers who spend too much time context-switching. If you're a developer constantly moving between an IDE, a terminal, and a dashboard just to log simple metrics, this saves you the clicks. It’s built for people who need fast, code-adjacent observability.
Monitors system health by using your agent to run post_classic_counter on event logs or post_json_stats when a deployment finishes.
Logs function execution counts, errors, and performance metrics directly from the coding environment without writing boilerplate logging code.
Updates business KPIs or tracks A/B test results by using natural language prompts that trigger post_ez_stat.
What Changes When You Connect
Directly log data from your chat client. You skip the steps of copy-pasting metrics into a separate dashboard or manually calling an SDK endpoint.
Use post_ez_stat for quick wins. Need to track 'User Signups'? Just name it, and StatHat handles creating the metric key automatically.
Process high volumes fast with post_json_stats. Instead of sending four separate messages for errors, latency, count, and status, you send them all in one JSON payload.
Track continuous performance data. Use post_classic_value to record things like average response time or system temperature, giving you true observability numbers.
Keep your workflow contained. Whether you're debugging from Cursor or running tasks from the terminal, all logging stays within the agent chat interface.
See it in action
Debugging Latency Spikes
A developer notices high latency during a specific API call. Instead of leaving the IDE to open monitoring tools, they prompt their agent: 'Log the current latency as 450ms for the 'checkout' stat key.' The agent runs post_classic_value, logging the exact metric without context switching.
Tracking User Flow Funnel
A data analyst needs to know how many users hit the pricing page today. They prompt: 'Increment the counter for 'Pricing Page Views' by 1.' The agent uses post_classic_counter and immediately updates the core metric, helping track conversion rates in real-time.
Post-Deployment Health Check
A DevOps engineer runs a batch test after deployment. They tell their agent: 'Report errors at 2 count, latency at 150ms value, and success status.' The agent executes post_json_stats, logging the full health snapshot with one command.
A/B Testing Results
An analyst runs a test comparing two features. Instead of maintaining separate spreadsheets, they prompt: 'Log the conversion rate for Group A as 0.12 and Group B as 0.15.' The agent uses post_classic_value to record both metrics simultaneously.
The honest tradeoffs
Using a counter for values
Trying to log 'Latency: 450' using the simple increment prompt. You accidentally use post_classic_counter instead of recognizing it needs a specific value.
If you need to track a number that isn't an event count (like latency or temperature), always use post_classic_value. This ensures your metric records the actual float/integer, not just adding 1.
Sending single stats in multiple prompts
Prompt 1: 'Log error count.' Prompt 2: 'Log latency value.' You are generating conversational noise and making logging inefficient.
When you have several related metrics (e.g., errors, counts, values), gather them all together and use post_json_stats in one prompt for maximum efficiency.
Assuming a stat exists
Writing a complex query that fails because the metric key 'User Activity' hasn't been defined yet, stalling your workflow.
For general metrics where you aren't sure if the key is set up, use post_ez_stat. It automatically handles creation, so you don't get stuck when logging a new type of metric.
When It Fits, When It Doesn't
Use StatHat if your primary need is simple, structured data logging—specifically tracking counts (events), values (time/metrics), or sending bulk snapshots. If your workflow involves this kind of direct observation and reporting, it's the right tool.
Don't use this server if you are trying to do advanced statistical modeling, like running a regression analysis, calculating standard deviations across an entire dataset, or performing time-series decomposition. This is not a data warehouse; it’s a logging endpoint. For those tasks, stick with dedicated analytical tools and feed the resulting metrics here.
Questions you might have
How do I log a simple event count with post_classic_counter? +
Use post_classic_counter when you just need to increment an integer, like counting successful logins. You provide the key and the change amount; it's perfect for discrete events.
What if I have several metrics (count, value, error) in one go? Which tool should I use? +
Use post_json_stats. This tool accepts a JSON array. It’s designed to handle multiple, different types of stats—counts and values—in a single request, keeping your logging clean.
Is post_ez_stat better than post_classic_counter? +
They serve different purposes. Use post_ez_stat when you need to log an entirely new metric without knowing if the key exists first. Use post_classic_counter when you know the stat already exists and just needs a simple increment.
Can I use post_classic_value for tracking user count? +
While technically possible, it's better practice to use post_classic_counter. Values are best for continuous data (like 45.5ms). Counters are meant specifically for whole, discrete event counts.
How do I log a batch of metrics using post_json_stats? +
You send a JSON payload containing multiple key/value pairs. For example: [{'key': 'errors', 'count': 5}, {'key': 'latency', 'value': 120}]. This is the most efficient way to log.
What keys do I need to authenticate when using post_ez_stat? +
You need your StatHat EZ Key or User Key. This key authenticates your AI agent and grants it access to the metrics endpoints, letting it log data without needing manual setup.
How do I manually control the timestamp when calling post_json_stats? +
You can include an optional 't' field within the JSON object. This allows you to specify a precise timestamp for that particular metric entry, overriding the system's default logging time.
What happens if I use post_classic_value with a stat key that doesn't exist? +
The API will return an error indicating an invalid or non-existent Stat Key. You must verify the spelling and format of your metric name before retrying the call.
What is the difference between the EZ API and the Classic API? +
The EZ API (post_ez_stat) allows you to post stats using a simple human-readable name and your EZ Key. The Classic API (post_classic_counter, post_classic_value) requires a unique Stat Key for each specific metric, providing more granular control.
Do I need to create a stat on the website before posting to it? +
No! When using the post_ez_stat tool, StatHat will automatically create the statistic for you if it doesn't already exist in your account.
Can I send multiple metrics at once to save time? +
Yes, you can use the post_json_stats tool to send an array of multiple stat updates in a single network request.
We've already built the connector for StatHat. Just plug in your AI agents and start using Vinkius.
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