AI & Machine Learning · Arize AX + Claude
Arize AX MCP with Claude: Debug and Improve Production AI Quality
The short answer
Claude connects to Arize AX through the Model Context Protocol (MCP), a secure standard that lets Claude read and act within a tool with permissions you control. Once connected, Claude can inspect traces, analyse spans, manage datasets, run evaluations and review experiments using plain-English requests, with access granted by an API key.
What is Arize AX?
Arize AX is an AI engineering platform used to trace, evaluate and improve AI applications. It helps teams see what their AI agents and chatbots are doing step by step, find where answers go wrong, and compare changes before they reach customers. Typical users include machine learning engineers, AI product teams, data scientists and operations leaders responsible for the quality of AI systems in production.
How Claude connects to Arize AX through MCP
The Model Context Protocol (MCP) is an open standard that works like a universal adapter between Claude and other software. It lets Claude use a tool securely, reading information and taking actions, with permissions the organisation controls. Arize AX provides an MCP server, and the connection can also be made through its direct API, so Claude can work with the platform without custom code for every request.
Once connected, Claude can typically inspect traces from your AI applications, analyse individual spans to see where a response went wrong, manage datasets, run evaluations and review experiments. A team member might simply ask Claude to find the slowest conversations from last week and explain why they failed, and Claude pulls that information from Arize AX and summarises it.
Permissions and approvals keep people in charge. Access is granted with an API key that the organisation issues and can revoke, and the connection is set up with least-privilege access so Claude sees only what it needs. Consequential actions, such as changing a dataset or launching an evaluation run, can be configured to require human approval before they proceed.
What you can automate
Investigating failed customer chats
When customers report poor chatbot answers, support leaders can ask Claude to pull the relevant traces from Arize AX, highlight the steps where the answer went off course and summarise the common causes. This turns hours of manual log reading into a short review that a team can act on the same day.
Comparing prompt and model changes
Product teams often test a new prompt or model version before release. Claude can review the experiments in Arize AX, compare quality scores between versions and explain which changes improved accuracy and which caused regressions, helping the team decide what to ship.
Building evaluation datasets
Good testing depends on good examples. Claude can help assemble and manage datasets from real production cases, such as collecting conversations where a human agent had to step in, so the team has a realistic set of tests for future evaluations.
Monitoring AI quality for leadership
Operations and department heads need a clear view of how AI systems perform. Claude can summarise evaluation results and trends from Arize AX into a plain-English weekly update, showing where quality is improving and where attention is needed.
Reviewing agent behaviour in multi-step tasks
AI agents that take several steps, such as looking up an order and then issuing a refund, can fail at any stage. Claude can analyse the spans for each step, pinpoint where the agent made a wrong choice and suggest what to test next, which speeds up debugging for engineering teams.
An end-to-end workflow: Weekly AI quality review for a customer support chatbot
TriggerEvery Monday morning, or when the customer satisfaction score drops below an agreed level
Claude pulls the past week's traces for the support chatbot from Arize AX through the connected API key.
Claude runs the agreed evaluations on those conversations and flags responses that scored poorly.
Claude cross-checks the flagged conversations against open tickets in the organisation's ticketing system to confirm which issues customers actually raised.
Claude drafts a summary of the top failure patterns, with example traces, and sends it to the support lead for review.
The support lead approves which failures should be added to the evaluation dataset, and Claude adds them once approved.
OutcomeTypically, the team spends a fraction of the time on quality reviews, catches recurring failures earlier and turns real customer problems into tests that protect future releases.
How we set it up
Free automation audit
We review your AI applications, support workflows and team priorities to identify where tracing and evaluation with Arize AX would save the most time or reduce the most risk.
Secure connection
We connect Claude to Arize AX using an API key, issued and controlled by your organisation, with least-privilege access so Claude can reach only the projects and data it needs.
Workflow design
We design the end-to-end workflow across Arize AX and your other systems, such as Microsoft 365, your CRM, ERP or ticketing tool, and agree which actions require human approval.
Testing on real cases
We test the workflow against past traces and real cases from your business to confirm the results are accurate and useful before anything goes live.
Launch and training
We launch with monitoring and audit logs in place, then train your team so they can use Claude confidently and know when to step in.
Who benefits
- Machine learning engineers
- Faster debugging of model and agent behaviour, with less time spent reading raw logs.
- AI product managers
- Clear evidence from experiments to decide which prompt or model changes to release.
- Customer support leaders
- Regular, plain-English reviews of chatbot quality and the failures that matter most to customers.
- Operations and IT heads
- Better oversight of AI systems, with audit logs and approval steps that keep people in control.
Security and control
- Access is granted with an API key that your organisation issues, monitors and can revoke at any time.
- Least-privilege access ensures Claude sees only the projects, datasets and traces it needs for the task.
- Consequential actions, such as changing datasets or launching evaluation runs, can require named human approval.
- Audit logs record what Claude accessed and did, supporting review and accountability.
- Each workflow is tested on real cases before launch, and performance is monitored after go-live.
For teams in the US, UK, India and the Middle East
Organisations in the United States and the United Kingdom can design workflows that align with their internal data governance and sector rules, with hosting chosen to match requirements. Companies in India must consider the Digital Personal Data Protection Act, 2023, when AI traces contain personal data. Businesses in the Middle East, including the UAE and Saudi Arabia, must follow their national data protection laws, so we select hosting regions and data handling to meet those requirements.
Learn more about Claude and MCP
Anthropic's AI model and assistant.
Secure links between Claude and business tools.
How engineers build and change software with Claude.
Hosted agents that run multi-step work.
The open standard Claude uses to connect to tools.
Frequently asked questions
What does the Arize AX MCP server do?
The Arize AX MCP server acts as a secure bridge between Claude and the Arize AX platform. It allows Claude to request information such as traces, spans, datasets and experiment results, and to carry out permitted actions, all through natural language. The organisation controls what the server can access.
Is connecting Arize AX to Claude secure?
The connection is designed around least-privilege access, meaning Claude sees only the data it needs. Access uses an API key that your organisation issues and can revoke. Human approval steps and audit logs add further control, though security always depends on how the connection is configured and maintained.
What can Claude do with Arize AX?
Typically, Claude can inspect traces from AI applications, analyse individual spans, manage datasets, run evaluations and review experiments. Users make these requests in plain English, and Claude summarises the results so non-technical team members can understand them.
How long does setup take?
Timelines depend on how many applications, data sources and approval steps are involved. A focused connection with one clear workflow can often be set up and tested within a few weeks, following the free automation audit and design stage. We confirm an estimate after reviewing your requirements.
What access or plan is needed to use Arize AX with Claude?
In general, you need an active Arize AX account and an API key with permissions suited to the work you want Claude to do. The exact access depends on your organisation's setup and the platform's current arrangements, so we confirm the requirements during the audit.