CJA MCP. Audit, report, and analyze omnichannel customer journeys.
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Adobe Customer Journey Analytics (CJA) MCP connects your AI agent directly to Adobe’s deep omnichannel insights. You gain control over data views, connections, and cross-channel reporting without needing to navigate complex web dashboards.
Your agent handles the entire journey analytics ecosystem—from listing available metrics for a specific view to running complex reports combining web, app, and offline customer behavior.
What your AI agents can do
Get data view dimensions
Lists all available descriptive attributes (dimensions) associated with a specific CJA data view.
Get data view metrics
Shows every quantifiable measurement (metric) that can be calculated for a particular data view.
Get report
Generates and retrieves a complex, cross-channel report based on defined criteria.
Requests a single report that pulls combined data from web, mobile app, and physical store interactions.
Lists all available dimensions (like 'Page Name' or 'Device Type') and metrics for any specified CJA data view.
Retrieves a list of active journey filters, allowing you to check their definitions or count them.
Lists and monitors the connections between your CJA environment and Adobe Experience Platform datasets.
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Supported MCP Clients
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Adobe Customer Journey Analytics (CJA) — 6 Tools
These tools give you granular control over every part of your CJA dataset: from listing all available metrics to compiling final, complex cross-channel reports.
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 Adobe Customer Journey Analytics (CJA) on Vinkius019d7547get data view dimensions
Lists all available descriptive attributes (dimensions) associated with a specific CJA data view.
019d7547get data view metrics
Shows every quantifiable measurement (metric) that can be calculated for a particular data view.
019d7547get report
Generates and retrieves a complex, cross-channel report based on defined criteria.
019d7547list connections
Provides an overview of all existing connections between your CJA environment and Adobe Experience Platform datasets.
019d7547list data views
Retrieves a complete list of every defined data view within the CJA account.
019d7547list filters
Lists all active journey filters, allowing you to check their name and definition.
Choose How to Get Started
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Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Start with Adobe Customer Journey Analytics (CJA), then connect any of our 4,800+ other servers whenever your AI needs more. One click, no limits.
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Adobe CJA. 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.
Today, getting a full picture of how a customer moves through your digital properties feels like detective work.
Right now, if you want to know the complete path a user took—say, they saw an ad on mobile, checked their account via desktop, and finally bought something in-store—you have to jump between multiple dashboards. You export data from one tab, copy dimensions into another spreadsheet, manually cross-reference it with segment definitions, and then hope you didn't miss a critical touchpoint.
With this MCP, that tedious process vanishes. Your agent reads the underlying model for you. Instead of clicking through tabs to piece together a journey, you simply ask your AI client to generate the report, pulling cross-channel data instantly.
You get full control over every element with the CJA MCP.
Manual auditing used to mean checking connections one by one and manually listing out all possible dimensions. Now, you run list_connections and list_data_views; your agent compiles a clean inventory of everything available in minutes.
The difference is the control. You aren't just getting an answer; you are validating the entire data foundation that makes the answer possible.
What you can do with this MCP connector
Connecting your AI client to this MCP lets you manage Adobe Customer Journey Analytics (CJA) from natural conversation alone. Forget logging into multiple dashboards just to piece together a user story. Your agent reads the data model for you; it can list all available dimensions attached to a view or check what metrics are possible across different touchpoints.
Need a cross-channel report combining web activity, app usage, and store transactions? You ask your agent, and it builds that complex query using the underlying connections. It's like having a dedicated data architect whispering instructions into your chat window. When you subscribe through Vinkius, you get instant access to this power, allowing you to audit everything—from checking the definition of every filter used for segmentation, to listing all active connections between CJA and Adobe Experience Platform datasets.
You just talk to it, and it manages your entire journey analytics setup.
019d7547-98a9-71bb-b9d1-71e789353156 
How CJA MCP Works
- 1 Subscribe to this MCP and provide your required credentials: Client ID, Client Secret, and IMS Organization ID.
- 2 Ask your AI client for a specific action; for example, 'What are the available metrics for my Global Web + App data view?'
- 3 The agent processes the request by querying the necessary CJA endpoints and delivers structured data back to you in plain text.
The bottom line is: your AI client handles the complex API calls, letting you get insights without ever touching a developer console.
Who Is CJA MCP For?
This MCP targets highly technical roles who need to understand data structure before they can report on it. If you're an analyst whose job involves knowing why the numbers look weird, this is for you.
Spends time listing and comparing dimensions across different data views to ensure reports are structured correctly.
Needs to monitor the complete customer journey flow, identifying friction points by checking active filters and connections on the fly.
Audits CJA data view configurations, using tools like list_data_views and get_data_view_dimensions to map out the entire data pipeline structure.
What Changes When You Connect
- You can audit your whole data setup by running list_data_views to see every view in the account. This prevents you from accidentally querying a deprecated or incorrect dataset.
- Never guess what numbers are available again. Use get_data_view_metrics to confirm exactly which metrics (like total revenue or session count) can be calculated for any given data view.
- Stop wasting time building reports in pieces. The get_report tool lets you combine web, app, and offline data points into a single request, giving you a true picture of the customer journey.
- Quickly validate your environment using list_connections to check if CJA is talking correctly to all necessary Adobe Experience Platform sources.
- Need to segment users? Use list_filters to see all defined segments. If you can't find a filter, you know exactly where the data gap is.
Real-World Use Cases
The New Product Launch Audit
A PM needs to validate if the new checkout flow captured all necessary data. Instead of manually checking logs, they ask their agent to run list_data_views and then get_data_view_dimensions on the relevant view to confirm every required field is available.
Finding User Drop-Off Points
A CX Manager notices a drop in mobile conversions. They ask their agent to list_filters, find 'Mobile Sessions', and then use get_report to pull cross-channel data that correlates web behavior with those sessions.
Debugging Data Discrepancies
A Data Engineer finds metrics don't match expectations. They ask the agent to run list_connections first, verifying all necessary AEP links are active before attempting any complex report generation with get_report.
Preparing for Quarterly Review
An Analyst needs a high-level summary of customer value. They use their agent to combine multiple data views and run the get_report tool, ensuring they capture both quantitative metrics via get_data_view_metrics and qualitative insights.
The Tradeoffs
Just running reports without checking definitions
Asking for a 'User Lifetime Value' report when the data view only captures transactional revenue. You get a flawed number because the underlying metric isn't available.
→ First, use get_data_view_metrics to confirm that 'Lifetime Value' or its component parts are actually measurable in your specific data view before attempting to run any reports.
Assuming all connections are active
Running a report and getting vague errors about missing sources. You waste time troubleshooting the reporting logic when the actual problem is broken authentication.
→ Always start by running list_connections to validate that CJA is successfully connected to every required Adobe Experience Platform source.
Mixing up filters and data views
Trying to define a new segment using filter parameters when the underlying raw dataset needs updating. The system fails because it's looking at metadata, not the primary view.
→ Use list_data_views first to find the correct base view name, then use list_filters on that view's context for accurate segmentation.
When It Fits, When It Doesn't
Use this MCP if your job requires knowing how data is structured before you can analyze it. If you are a highly skilled analyst or engineer who needs to validate the entire dataset—from checking every connection using list_connections, to confirming available metrics via get_data_view_metrics, and finally compiling everything with get_report—this tool is essential. Don't use this if your only need is to view a simple dashboard; those tools abstract away the complexity you need to manage. If all you want is 'a report,' but you don't know what metrics are possible, start by using get_data_view_dimensions and list_data_views first.
Common Questions About CJA MCP
How do I check if a specific metric exists using get_data_view_metrics? +
You run get_data_view_metrics and specify the exact data view. The agent returns a list of every available measurement, so you can confirm it's there before building any reports.
Should I use list_data_views or get_report first? +
Always start by running list_data_views. This gives you the current names and status of all data views, ensuring you are basing your report on an active dataset.
What is the difference between list_filters and get_report? +
list_filters shows you the definitions of existing user segments. get_report uses those segment definitions (or other criteria) to pull live, aggregated data into a final report.
Does this MCP help me audit my connections? Use list_connections. +
Yes, that's exactly what it does. Running list_connections verifies if your CJA setup is correctly linked to all necessary Adobe Experience Platform sources without needing manual checks.
Before I run a large cross-channel report, should I use get_report or list_data_views first? +
You should always start with list_data_views. This command shows you all the available data view IDs and names in your CJA account. Once you confirm the correct ID for your source material, then you can pass it to get_report for accurate analysis.
If my cross-channel report fails or returns empty results, how do I use get_report to debug the issue? +
A failure usually means an incorrect view ID or a missing scope. The error response will detail which parameter is invalid or requires updated permissions. Check your CJA credentials and ensure the data source exists first.
I need to know what specific fields I can track. How do I use get_data_view_dimensions? +
Use get_data_view_dimensions against a view ID to list every available dimension for that dataset. This tells you exactly which metrics, like 'Page Name' or 'User ID', are measurable in your analysis.
When troubleshooting data gaps across multiple devices, should I check my filters or use list_connections? +
Always start with list_connections. This confirms that your CJA environment has an active link to the necessary Adobe Experience Platform datasets. If connections are broken or missing, no amount of filtering will fix the underlying data gap.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.