Step 01
Ask
A customer asks a question in Claude or ChatGPT, in plain words.
Built with
- Amazon Bedrock AgentCore
- Amazon Cognito
- AWS IAM
- Amazon CloudWatch
The gap
A great SaaS app isn’t enough.
Not if your users can’t reach their data where they already work.
Your customers ask Claude and ChatGPT for help all day. When their data lives only inside your app, the assistant can’t see it, so they copy it across by hand or go without. An MCP server closes that gap.
Your SaaS appGreat inside the appOnly reachable when users log in and go looking.
Dashboards
Reports
Customer data
Where your users work
ClaudeChatGPT
What were our top accounts last month?
I can’t see your data. Can you paste it in?
Why it matters
Why it matters.
Your users already work in Claude and ChatGPT. If their data isn’t there, they copy it across by hand, or leave it out.
An MCP server turns the APIs you already have into tools any assistant can call, so you open an AI channel without rebuilding your product.
Built on AWS with OAuth sign-in and per-customer access, it’s an AI feature your security team and your enterprise customers can sign off.
How it works
Ask in Claude. Answered by your product.
Every question takes the same four steps, in seconds. Your customer never leaves the chat, and your data never leaves your control.
Your customer
Claude · ChatGPTHow did our sales track this quarter?
AWS · Your account
MCP server
On Amazon Bedrock AgentCore
Checks who is asking and what they can see
Signed in · Only their data
Your product
Your existing APIsNo rebuild needed
Your dataStays where it is
Read-only · Every call logged
Step 02
Check
The MCP server on AWS confirms who they are and what they are allowed to see.
Step 03
Fetch
It calls your existing APIs for that customer’s data, and only theirs.
Step 04
Answer
The answer lands back in the chat as text, a table or a chart.
1 / 4
The real value
Not just your data. The context only you have.
Claude and ChatGPT know a little about everything. Your product knows your niche: what the numbers mean, how they connect and which questions matter. An MCP server turns that know-how into tools, so every answer is right for your industry, not just plausible.
The same question“How did placements, fees and margin trend this year? Chart it.”
A general assistant, on its ownA plausible guess
- Guesses what “margin” or “active” means in your industry
- Works only from whatever the user pastes in
- Can’t tell one customer’s data from another’s
The same assistant, with your MCP serverAn answer from your niche.
- Your definitions: the metrics your industry runs on, counted the way your customers trust
- The right tool for each question: a KPI summary, a leaderboard, a filtered list or an export
- Only the signed-in customer’s data, every time
What you bring that no model has
01Your data modelThe data you already pull together from many systems, in one shape an assistant can use.
02Your domain logicThe definitions, KPIs and rules your customers trust, built into every tool.
03Your workflowFrom a question to a chart, a CSV or a client-ready deck, without leaving the chat.
Anyone can plug in a model. Only you can give it your niche.
What’s included · how it runs
Map. Secure. Build. Launch.
Step 01
Map
Pick the questions customers ask, and the APIs and data that answer them.
What’s included
Use case and API review
Agree the first use case, the tools the MCP server needs and the data each tool can reach.
Step 02
Secure
Choose sign-in and the gateway: OAuth, scoped tokens and per-customer access on AWS.
What’s included
Sign-in and access design
OAuth sign-in through your own identity provider, scoped tokens, and access checked on every call so each customer only sees their own data.
Step 03
Build
Turn your REST APIs into MCP tools on Amazon Bedrock AgentCore, starting from what you have today.
What’s included
MCP server on AgentCore
AgentCore Gateway turns your existing APIs into MCP tools, with Python tools for your data team where you need them.
Answers that render as apps
MCP Apps put dashboards and tables right in the conversation, in Claude and ChatGPT.
Step 04
Launch
Connect Claude and ChatGPT, then hand over monitoring, audit logs and running costs.
What you walk away with
- An MCP server your customers connect from Claude and ChatGPT
- OAuth sign-in with per-customer access
- Read-only tools over your existing APIs
- Audit logs and monthly running costs