AI & Machine Learning · FutureAGI + Claude
FutureAGI MCP with Claude: Test, Trace and Improve Your AI Systems
The short answer
Claude works with FutureAGI through the Model Context Protocol (MCP), a secure standard that lets Claude use the tool with permissions you control. Once connected, Claude can help run evaluations, inspect observability traces, manage datasets and review annotations using plain English requests, with people approving any consequential changes.
What is FutureAGI?
FutureAGI is an AI evaluation and observability platform. Teams use it to test, trace, simulate and improve AI systems such as chatbots and AI agents. Typical users include AI engineers, product teams, quality leads and operations managers who need to measure how well an AI system performs, find why it fails and track improvements over time. It helps organisations manage evaluation datasets and close the loop with human annotation workflows.
How Claude connects to FutureAGI through MCP
The Model Context Protocol, or MCP, is an open standard that acts like a universal adapter between Claude and other software. It lets Claude securely read information from a tool and take actions in it, using access that the organisation sets and controls. For FutureAGI, the connection can use the platform's MCP server or its direct API, signed in with an API key.
Once connected, Claude can typically help your team ask questions in plain language about evaluation results, look up observability traces to see where an AI agent went wrong, pull up datasets and summarise annotation reviews. This means a quality lead can ask, in one sentence, which recent conversations scored poorly and why, without building reports by hand.
Permissions and approvals keep people in charge. Access is limited to what each role needs, following least-privilege principles. Actions that change datasets, evaluation settings or annotation outcomes can be set to require human approval before they take effect, and every request is recorded in an audit log for review.
What you can automate
Regression checks after a model update
When an engineering team changes a prompt or swaps a model, Claude can start an evaluation run against a standard dataset and summarise which test cases got worse. Product managers receive a short, plain-English report rather than raw scores, and the release decision stays with the team.
Debugging failed customer conversations
A support operations manager can ask Claude to find recent chatbot conversations flagged as low quality, then review the matching traces. Claude can point out the step where the bot misread the question, helping the team fix the root cause faster.
Building and maintaining evaluation datasets
Quality leads often spend hours collecting example questions and expected answers. Claude can help organise these into well-labelled datasets, flag duplicates and suggest gaps in coverage, with changes reviewed before they are saved.
Closing the loop with annotation reviews
When human reviewers grade AI outputs, Claude can summarise their annotations, highlight where reviewers disagree and draft a list of issues for the engineering backlog. This keeps human judgement at the centre while speeding up the review cycle.
Weekly quality reporting for leadership
A head of operations can ask Claude for a weekly summary of AI system performance, covering evaluation trends, notable failures and open annotation work. The report is ready for a management meeting without anyone pulling data from several screens.
An end-to-end workflow: Weekly AI quality review for a customer support chatbot
TriggerEvery Monday at 8 am, or when a new model version is deployed
Claude runs an evaluation against the customer support test dataset in FutureAGI
Claude pulls traces for conversations that scored below the agreed threshold
Claude groups the failures by cause, such as wrong policy answers or missed escalations
Results and a draft summary are posted to a Microsoft 365 Teams channel for the quality team
Open issues are logged as tickets in the team's service desk, pending approval by the quality lead
Reviewer annotations on flagged cases are reflected in the next week's dataset
OutcomeThe quality team starts each week with a clear list of what is going wrong, why, and what to fix first, cutting manual review time and shortening the gap between a failure and its fix.
How we set it up
Free automation audit
We review your AI systems, evaluation practices and team workflows to identify where FutureAGI and Claude can save the most time, at no cost.
Secure connection
We connect Claude to FutureAGI using the platform's MCP server or direct API, signed in with an API key stored in a secure vault. Access is limited to the projects and actions your team needs.
Workflow design
We design the end-to-end workflow across FutureAGI and your other systems, such as Microsoft 365, CRM, ticketing or ERP, and decide which actions need human approval.
Testing with real cases
We test the workflow against past conversations and evaluation runs from your own history to confirm the results are accurate and useful before anything goes live.
Launch, monitoring and training
We launch with audit logs and monitoring in place, then train your team so they can use Claude confidently and know when to step in.
Who benefits
- AI and engineering leads
- Faster diagnosis of failures and quicker checks after each model or prompt change.
- Quality and QA managers
- Clear, regular reports on AI performance without manual data gathering.
- Product managers
- Plain-English answers about what is improving and what still needs work.
- Operations and support heads
- Early warning when customer-facing AI starts to underperform.
Security and control
- Least-privilege access, so Claude only reaches the FutureAGI projects and actions each person is allowed to use
- Human approval required for changes to datasets, evaluation settings or annotation outcomes
- Full audit logs of every request Claude makes and every action taken
- API keys stored in a secure vault and rotated on a regular schedule
- Regular review of connection permissions as roles and projects change
For teams in the US, UK, India and the Middle East
Organisations in the United States and the United Kingdom can host the connection in a region that matches their data handling requirements. Companies in India must consider the Digital Personal Data Protection Act, 2023, and we design data flows to support it. Organisations in the UAE and Saudi Arabia are subject to their national data protection laws, and we select the Claude hosting region to meet those requirements. The right hosting region is chosen during the audit to match your legal and contractual obligations.
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 FutureAGI MCP server do?
The FutureAGI MCP server lets Claude connect to the platform in a standard, secure way. Through it, Claude can typically run evaluations, look up observability traces, manage datasets and review annotations. It works as a bridge, so your team can ask for these tasks in plain language instead of navigating every screen.
Is it secure to connect FutureAGI to Claude?
Security depends on how the connection is set up. We use an API key stored in a secure vault, limit access to the projects each person needs, and require human approval for changes that matter. Every request is logged, so your team can review what happened and when.
What can Claude do with FutureAGI once it is connected?
Typically, Claude can help run evaluations, review traces to understand failures, organise datasets, summarise annotation work and produce quality reports. What it can do in your setup depends on the permissions you grant and the workflow we design together.
How long does setup take?
The timeline depends on how many systems are involved and how complex your workflows are. A simple connection with a few use cases can often be ready in a short period, while larger end-to-end workflows take longer because they include testing with real cases and team training. We confirm the timeline after the free audit.
Which plan or permissions do I need?
You need an active FutureAGI account and an API key with access to the projects you want Claude to use. The exact plan and permission levels depend on your FutureAGI agreement. During the audit, we check what your account supports and recommend the least-privilege setup for your team.