Sensors Data MCP. Analyze every click and conversion path in natural language.
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Sensors Data connects your AI agent directly to a professional big data analytics platform. It provides ten tools for analyzing user behavior: calculate retention rates (`analyze_retention`), map conversion funnels (`analyze_funnel`), and retrieve deep behavioral profiles for specific users.
Use it to query event metadata, check data pipeline health, and understand how people actually use your product.
What your AI agents can do
Analyze events
Performs detailed analysis on raw event data, allowing you to understand user interactions in depth.
Analyze funnel
Calculates conversion metrics across predefined steps or stages of the user journey.
Analyze retention
Determines how many users return to the platform over defined time periods, calculating retention rates.
Runs funnel analysis tools (analyze_funnel) to calculate conversion metrics across defined user paths.
Determines how many users return over time using analyze_retention, giving you the core health metric of your product adoption cycle.
Uses get_event_schema or list_events to understand exactly what properties and data fields are available for any given event type.
Pulls the complete, chronological sequence of actions taken by a user using get_user_behavior_sequence or query_behavior_list.
Looks up specific users (lookup_user) to pull their profile attributes, such as category or value status.
Checks the data stream's health and ingestion rates to confirm your analytics pipeline is running without errors.
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Sensors Data: 10 Tools for Behavioral Analytics
Use these ten tools to query user profiles, calculate conversion metrics, and reconstruct user journeys directly through natural language prompts.
019d847canalyze events
Performs detailed analysis on raw event data, allowing you to understand user interactions in depth.
019d847canalyze funnel
Calculates conversion metrics across predefined steps or stages of the user journey.
019d847canalyze retention
Determines how many users return to the platform over defined time periods, calculating retention rates.
019d847cget event schema
Retrieves a full list of properties and data fields associated with a specific event type.
019d847cget project info
Gets metadata about the current Sensors Data project, useful for verifying configuration settings.
019d847cget user behavior sequence
Pulls the exact chronological order of events a user performed, recreating their path through your product.
019d847clist events
Lists all unique event names currently defined within your project's schema.
019d847clist user properties
Retrieves a list of all available attributes and fields used in user profiles.
019d847clookup user
Gets the full profile information for any specific user ID provided.
019d847cquery behavior list
Retrieves a generalized list of user behaviors or events, useful for broad trend analysis.
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- Works with Claude, ChatGPT, Cursor, and more
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What you can do with this MCP connector
Yo, listen up. This Sensors Data MCP Server connects your AI agent straight to a professional big data analytics platform—no custom queries needed. It gives you ten tools designed for analyzing user behavior, letting you understand exactly how people are using your product.
When you use this server, your agent can calculate conversion metrics across defined paths by running analyze_funnel. If figuring out if users stick around is what you need, you call analyze_retention; that tool spits out retention rates for specific time periods. You'll also get a general idea of user activity using query_behavior_list or seeing detailed raw interactions with the analyze_events tool.
To map a single person’s entire journey, you pull the exact chronological sequence of actions they took by running get_user_behavior_sequence. For those deep dives into what properties and data fields are available for any given event type, you check get_event_schema, or if you need to see every unique event name currently defined in your project's schema, you use list_events.
When it comes to user profiles, you look up specific users using lookup_user—that gives you their full profile attributes, like what category they belong to or if they're a high-value status. If you need to know all the available fields that can be used in any user profile across your system, run list_user_properties.
For verifying your data structure and project setup, you grab metadata about the current Sensors Data project with get_project_info, which confirms your configuration settings. You'll also find a list of all available attributes that define raw event metadata using list_user_properties.
Essentially, this server lets your agent break down user activity into three main areas: measuring how people move through the product using funnel and retention analysis; mapping out individual paths or broad trends using sequence and event tools; and verifying data integrity by checking schemas, listing available fields, and confirming project metadata.
How Sensors Data MCP Works
- 1 First, you need a Project Name and API Token. You get these by navigating to the 'API Access' section in the Sensors Data Dashboard.
- 2 Next, your AI client uses natural language instructions (e.g., 'Show me retention for last quarter') which are translated into tool calls like
analyze_retention. - 3 The server executes the call against the live data platform and sends back structured results—like a list of top events or a calculated conversion rate.
The bottom line is: you tell your AI client what question to ask, and it handles the complex connection and query logic to pull the answer directly from your raw event logs.
Who Is Sensors Data MCP For?
This server is for data people who are tired of jumping between dashboards or writing boilerplate SQL just to find a simple trend. It's built for the Product Manager who needs quick answers on adoption rates, and the Growth Engineer who must link behavioral metrics directly into automated marketing flows.
Uses this tool to monitor which features users are adopting in real-time. They run analyze_funnel to see if users drop off between viewing a product and adding it to the cart.
Automates metric retrieval. Instead of writing complex queries for user behavior summaries, they call tools like list_events or analyze_retention via natural language prompts.
Integrates deep behavioral insights into automated flows. They pull specific attributes using lookup_user to segment users for personalized marketing campaigns.
What Changes When You Connect
- Stop writing complex SQL for basic metrics. With
analyze_retention, you get the direct metric of user stickiness—how many users come back after 30 days—without touching a database query. - Map out drop-off points instantly. Instead of guessing where your sales funnel leaks, use
analyze_funnelto calculate precise conversion rates between steps (e.g., 'View Product' to 'Checkout'). - Understand the user's story. The
get_user_behavior_sequencetool builds a perfect timeline of events for any single ID, letting you see exactly what they did right before they left. - Avoid schema confusion. Before writing anything, run
list_eventsorget_event_schema. This tells your agent the exact properties and fields available for that event type, keeping your queries clean. - Get context on the fly. Use
lookup_userto pull a user's core profile details (like 'High-Value Customer') alongside their recent activity, making segmentation trivial.
Real-World Use Cases
Diagnosing Feature Failure
A Product Manager noticed that signups are high but paid conversions are low. Instead of guessing, they ask the agent to run analyze_funnel from 'Sign Up' to 'Purchase'. The result shows a massive drop-off at the payment screen, pointing directly to an issue with the checkout flow.
Identifying Power Users
A Growth Engineer needs to understand what makes their most valuable users different. They use lookup_user on several top accounts and cross-reference those results with list_user_properties to find a common attribute, like 'Uses the Reporting Module,' that defines high value.
Investigating Abandoned Sessions
A Data Analyst finds an unusual spike in user activity but no corresponding sales. They ask the agent for get_user_behavior_sequence on a few affected IDs, which reveals that users were repeatedly getting stuck on a specific screen flow.
Checking Data Integrity
A DevOps team member is worried about data quality. They run the pipeline health check and use get_project_info to verify connectivity and token validity before starting any analysis, preventing bad reports.
The Tradeoffs
Guessing event schemas
Trying to run an analysis on a field name like 'user_purchase_amount' when the actual schema calls it 'transaction.value'. The query fails because the agent doesn't know the exact property.
→
Always check the available fields first. Run get_event_schema or use list_user_properties to verify the precise field names before building your analysis prompt.
Overloading a single query
Asking, 'Give me everything about users and their funnels.' This vague request results in an unmanageable blob of data that doesn't answer a specific question.
→
Break it down. First, run analyze_retention to define the scope (e.g., 'last 30 days'). Then, use analyze_funnel with those defined parameters for a focused result.
Ignoring project context
Trying to access data from an old or disconnected analytics environment, leading to stale results that don't reflect current product usage.
→
Always confirm your setup by calling get_project_info first. This confirms the active Project Name and Base URL are correct for the analysis.
When It Fits, When It Doesn't
Use this server if your core problem is understanding why users behave the way they do—you need to map sequences, calculate conversion rates, or determine long-term stickiness. It's perfect when you have raw event data and need insights without writing complex SQL.
Don't use it if: 1) You only need simple CRUD operations (like just fetching a single user name); those are better handled by dedicated database connectors. 2) Your goal is purely operational alerting on infrastructure health; while get_project_info helps, specialized monitoring tools handle that better.
If you're stuck between 'Do I query events or profiles?'—use this server anyway. The combination of analyze_events, list_user_properties, and lookup_user lets you cross-reference both the what (the event) and the who (the user profile) seamlessly.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Sensors Data. 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 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
The Old Way: Hunting for a Single Conversion Leak
Today, figuring out why users stop buying is a nightmare. You have to jump into the analytics dashboard, set up a custom filter for 'Viewed Product' and then manually link it to another report showing 'Add to Cart.' If you want to know which category was the biggest drop-off point last month? You spend hours writing SQL joins across multiple tables just to get a single number.
With this MCP server, your agent handles the plumbing. You just ask: 'What's the conversion rate from product view to purchase for Q2?' The tool runs `analyze_funnel` and gives you one clear answer, instantly showing exactly where your funnel leaks.
Using Sensors Data with get_user_behavior_sequence
Before this server, tracing a user's path was like detective work. You’d pull their ID, then run one query for 'Viewed X,' another for 'Clicked Y,' and manually stitch them together in Excel to get the true sequence. This process is slow, error-prone, and takes half a day.
Now, you tell your agent to 'Show me the full chronological path for this user.' It runs `get_user_behavior_sequence` and hands back the entire story—every click, every view, in perfect order. You just read it.
Common Questions About Sensors Data MCP
How do I check if a certain field exists for my events using get_event_schema? +
You call get_event_schema and provide the event name you are interested in. The server returns a list of all available properties, confirming exactly what data fields you can query.
What is the difference between list_events and query_behavior_list? +
list_events gives you the names of all possible event types (e.g., 'login', 'checkout'). query_behavior_list actually retrieves a list of past user actions, using those defined events.
Can I use analyze_funnel to compare two different product lines? +
Yes. You can define separate funnels for distinct groups by providing the specific criteria or segments in your prompt and letting the agent execute multiple analyze_funnel calls.
What data do I need to run analyze_retention? +
You must specify a time period (e.g., 'the last 90 days') and the definition of an active user, as analyze_retention needs boundaries to calculate returning users.
Do I need an API key for lookup_user? +
Yes. The tool requires a valid Project Name and API Key (Token) for authentication before it can pull profile data for a specific user ID.
When I use `get_project_info`, what specific metadata do I get about my Sensors Data project? +
It returns key project settings, including your API version and the current status of the data source. This is useful for confirming that your connection parameters are correct before running any heavy analysis.
How can I use `get_user_behavior_sequence` to limit my event list to a specific date range? +
You must include start and end timestamps in the request payload. The tool then returns the user's events strictly within that time window, allowing you to focus on behavior from a single day or week.
If `analyze_events` fails with an error code, what should I check first? +
The response will detail if the failure is due to invalid parameters, schema mismatches, or resource limits. Always verify your project name and required event properties before re-running the query.
Can I automatically retrieve the behavioral profile for a specific user ID? +
Yes! Use the get_user_profile tool with the specific User ID. Your agent will return all recorded attributes and recent behavioral events associated with that user.
How do I monitor the data ingestion status via the AI agent? +
Use the get_ingestion_health tool. The agent will retrieve real-time statistics on data volume, successful ingestions, and any flagged errors in your pipeline.
Can I list all events tracked in a specific project? +
Yes! Use the list_events tool. Your agent will return a list of all event names and their associated metadata currently configured in your Sensors Data project.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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