EyePop.ai MCP. Turn video feeds into actionable data points.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
EyePop.ai brings computer vision to your agents. It lets you analyze images and videos using pre-trained models for object detection, face recognition, and visual classification.
You can programmatically manage entire visual pipelines, getting structured metadata like bounding boxes and confidence scores directly through conversation.
What your AI agents can do
Analyze image
Runs object recognition on a single photo, returning bounding boxes and labels for everything it finds.
Analyze video
Processes video files to identify and track objects over time in the stream.
Check eyepop status
Verifies if your API connection to EyePop is currently active and working correctly.
Analyzes a single picture and returns labels, detected objects, and their precise locations.
Runs temporal object detection on entire videos, providing results over time.
Creates or lists the specific processing streams (Pops) needed for your monitoring setup.
Checks the real-time operational health of the EyePop API connection and visual processes.
Fetches account information, model details, or specific pipeline metadata when you need to verify settings.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
EyePop.ai with 10 Tools
These tools let you manage entire visual pipelines, analyze media streams, and programmatically access detailed computer vision data for images and video.
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 EyePop.ai on Vinkius019dd0edanalyze image
Runs object recognition on a single photo, returning bounding boxes and labels for everything it finds.
019dd0edanalyze video
Processes video files to identify and track objects over time in the stream.
019dd0edcheck eyepop status
Verifies if your API connection to EyePop is currently active and working correctly.
019dd0edcreate pop
Sets up a new visual processing pipeline for monitoring specific types of media streams.
019dd0edget account
Retrieves basic account details to confirm your user setup information.
019dd0edget model
Fetches detailed information about a specific visual detection model.
019dd0edget pop
Retrieves the current details and status of an existing visual pipeline.
019dd0edlist detections
Gets a list of all historical object detections that have been recorded by your system.
019dd0edlist models
Provides a catalog of all the available visual models you can use for detection.
019dd0edlist pops
Lists every active and inactive visual pipeline set up in your account.
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 EyePop.ai, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,000+ 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 EyePop.ai. 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
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 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Sifting through hours of raw security footage is brutal.
Today, if you need to know what happened in a specific area of a store or on a busy street corner, someone has to watch the video. That means sifting through hundreds of minutes of unedited footage, frame by agonizing frame, just trying to spot anomalies or count people.
With this MCP, your agent handles the viewing process. You tell it what you're looking for—say, all instances of 'Persons'. It processes the stream and gives you a clean report with every detection metadata point you need. The time commitment drops from days to minutes.
Getting structured data using `analyze_image`
Manual analysis requires drawing boxes around objects and then labeling them, a process that is slow and inconsistent. You have to copy-paste the coordinates and labels into a spreadsheet yourself.
Now, your agent runs `analyze_image`. It returns the bounding box coordinates and classification IDs in a clean data format you can use immediately. It just works.
What you can do with this MCP connector
Think about manually reviewing video footage or analyzing thousands of photos—it’s slow, tedious work. This MCP lets your agent take over the heavy lifting. Instead of watching every frame, you just point it at the media, and it handles the visual intelligence. You can ask it to list all active visual pipelines for a whole site, track specific objects in real time, or even get detailed metadata about everything it spots.
It's like having a dedicated computer vision architect hooked up to your agent via Vinkius. When you need to monitor status or check if the connection is working, it tells you immediately. No more missing critical object detections; your AI acts as the coordinator.
019dd0ed-f984-7039-9dbc-cd10ba82e5f9 
How EyePop.ai MCP Works
- 1 Subscribe to the MCP and pull your EyePop.ai API Key from your dashboard.
- 2 Connect this MCP to any compatible agent through Vinkius, giving your AI access to vision tools.
- 3 Ask your agent to perform a specific task, like analyzing video or listing all pipelines. The agent handles the connection details for you.
The bottom line is that you just talk to your agent, and it manages the complex communication with EyePop.ai behind the scenes.
Who Is EyePop.ai MCP For?
This MCP is built for people who deal with large volumes of visual data—security analysts reviewing feeds, retail managers tracking foot traffic, or developers integrating CV features into custom apps.
Needs to instantly pull detection summaries and monitor visual alerts across multiple cameras without opening a dashboard.
Requires tracking customer behavior, like foot traffic metadata, directly from images or video feeds in their workflow.
Needs to integrate high-speed visual data into custom monitoring tools by calling specific endpoints for object detection and coordinates.
What Changes When You Connect
- You stop manually reviewing footage. Instead, your agent analyzes the media and gives you structured JSON output with precise bounding box coordinates for every detected item.
- Monitoring is instant. Use tools like
check_eyepop_statusto quickly verify API connectivity before starting a major analysis run. - It manages complex visual streams automatically. You can use
create_popandlist_popsto build or check your entire detection setup in one conversational turn. - You never lose metadata. By calling
list_detections, you get a historical record of everything seen, including confidence scores for every object found. - It’s highly scalable. Use
get_accountand other status tools to monitor the volume of processing without worrying about resource limits.
Real-World Use Cases
Auditing a large site
The ops manager needs to know what happened in three weeks of video footage. They ask their agent to list_pops first, then tell it to run analysis on the feed using analyze_video. The system returns a structured list detailing all detected people and vehicles.
Debugging an alert
A developer gets a suspicious object detection in their app. They use their agent to call list_detections for that specific feed, retrieving the confidence scores to determine if the detection is reliable enough to act on.
Setting up a new monitoring zone
The retail analyst needs to track flow in a new area. They ask their agent to use create_pop with the necessary parameters, establishing a dedicated visual pipeline for that specific camera feed.
Checking system health
Before running an end-of-month report on all visual data, the developer first runs check_eyepop_status to ensure their API key hasn't expired and the connection is solid.
The Tradeoffs
Treating it like a simple image tool
Only running analyze_image when you have video data. You miss out on the temporal context (how objects move or change over time).
→
Always use analyze_video if your source is a recording. This handles the full timeline, giving you continuous object detection results across every frame.
Ignoring resource management
Running multiple large video analyses without checking system capacity, leading to unexpected failures or rate limits.
→
First, check list_pops and then use get_pop on the target pipeline. This lets you verify its current status before committing compute resources.
Just describing objects
Only getting a list of labels without coordinates or confidence scores, making the data useless for mapping.
→
Ensure your query asks for bounding boxes and classification IDs. This detailed metadata is essential; it comes standard when you use analyze_image.
When It Fits, When It Doesn't
Use this MCP if your problem involves visual context—if the data source is a picture or video, you need this. You want to know where an object is and what it is doing over time. Don't use this if you only need basic text summaries (e.g., 'Three people were seen'). For pure text processing, use standard language model tools. But if that text summary must come from analyzing a camera feed or video stream, then EyePop.ai provides the necessary visual intelligence layer to your agent.
Common Questions About EyePop.ai MCP
How do I analyze video with EyePop.ai using analyze_video? +
You call analyze_video and provide the file path or stream link to your agent. The agent manages the temporal process, returning detection results for every object across the entire video timeline.
What is the difference between list_models and get_model? +
list_models gives you a catalog of all available visual models (the types). get_model retrieves the specific, detailed settings or parameters for one model once you've chosen it.
Can I check if my API key works before running analyze_image? +
Yes. Before any big job, run check_eyepop_status. This verifies your connection health and ensures the service is operational for you right now.
How do I list all my active visual pipelines? Use list_pops. +
To see everything set up, simply use list_pops. This gives you a full inventory of every 'Pop' (pipeline) running in your account.
What does running `check_eyepop_status` confirm about my EyePop connection? +
It gives a real-time operational status of your API connectivity and service health. This is the best way to verify if your account can handle complex visual tasks before you run them.
How do I set up a new, dedicated visual pipeline using `create_pop`? +
You call create_pop and provide the necessary configuration details. This initializes a specific, named 'Pop' (Pipeline) within your account for ongoing, structured analysis.
After an image or video run, how do I get the detailed object bounding box coordinates using `list_detections`? +
You pass the relevant Pop ID to list_detections. The tool returns a structured list containing all detected objects and their precise boundary coordinates (bounding boxes) for data use.
What kind of usage quotas can I view by calling `get_account`? +
This function fetches your core profile information alongside critical billing and usage quota details. It helps you monitor service scaling and track remaining credits or limits.
How do I find my EyePop.ai API Key? +
Log in to your account, click on your profile, and copy your unique API Key from the developer section.
Can I check object detection results via AI? +
Yes! The list_detections tool allows your agent to retrieve metadata including object classes and confidence scores for any media stream.
How do I list my active Pops? +
Use the list_pops tool to retrieve your complete directory along with the unique identifiers for all managed visual pipelines.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.