Platform
AI Agent Orchestration System
Coordinates multiple agents, tools and handoffs in one runtime.
Platform
MCP Server Build
AI assistants such as ChatGPT and Microsoft Copilot can use your product or data safely, through one secure MCP server.
Customers and staff now ask whether AI assistants can work with your product or data. This build gives them one secure way in. It is a remote MCP server for one system, with up to eight tools you agree in advance. It is read-only by default, signs users in with OAuth or scoped keys, and logs every action. It runs in your own cloud account.
- Up to eight tools designed around your own system
- Read-only by default, with confirmed write actions only
- Sign-in, rate limits and an action log built in
$3,900
20–30 working days
Optional maintenance $249/mo
What it is
MCP Server Build is a production Model Context Protocol server for one of your systems, either your API or your database. It exposes up to eight tools, read-only by default, with up to three write actions that need confirmation. Sign-in uses OAuth or scoped API keys, with rate limits and a log of every action.
Why businesses buy it
Connecting AI assistants to a business system is easy to do badly. A rushed connection can expose too much data or let an assistant change records nobody meant it to touch. This build starts with a written tool design, keeps access read-only unless you approve a write action, and logs every call. Your developers get a README to maintain it. The Scope Card fixes the system, tool count, write actions and testing before you order, so the work cannot drift.
Problems this solves
Customers ask whether your product works inside AI assistants
One remote MCP server exposes up to eight tools for your product. It is tested on twenty agreed tasks in two assistants, such as ChatGPT and Microsoft Copilot.
Letting an AI assistant touch live data feels risky
Tools are read-only by default, and up to three write actions need confirmation and your security sign-off. Rate limits and an action log record every call the assistants make.
Your team has no experience building MCP servers
We write the tool design document and deploy the server to your cloud. You get a developer README, a user setup guide and a recorded session.
A good fit if
Customers keep asking whether your product works inside AI assistants like ChatGPT or Microsoft Copilot.
Internal teams want AI assistants to look things up in your CRM, database or internal system.
Your system has a stable API with authentication, or a documented database schema.
You want read access first, with any write action controlled and logged.
Not the right fit if
You need one controlled entry point for every AI model your company uses. See AI Gateway.
You want guardrails and monitoring across agents you already run. See AI Agent Security Layer.
You need role-based permissions for many agents and users. See AI Agent Permission System.
You want a full assistant built for staff, not a connection for existing assistants. Start with an expert consultation.
How it works
- We review your API documentation or database schema and agree up to eight tools in a tool design document.
- Each tool is read-only by default; up to three write actions are added only with a confirmation step and your security sign-off.
- We build one remote MCP server with OAuth or scoped API key sign-in, rate limits and an action log.
- The server is deployed to your own cloud account, such as AWS, Google Cloud, Azure or Cloudflare.
- We test twenty agreed tasks in two AI assistants, such as ChatGPT and Microsoft Copilot, and record the results.
- You get a developer README, a user setup guide and a recorded handoff session.
What you provide within 72 hours
Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.
- API documentation or a database schema for the system the server will connect to.
- A test account and a sandbox we can safely run tasks against.
- Access to your cloud account, shared by inviting our team or creating a key you control.
- A stable API with authentication already in place.
- Security sign-off on each write action before it is built.
- Your business information (company basics, hours and brand voice), shared during intake.
- One decision-maker who approves the tool design and test results.
- An API key for your own AI model account (for example OpenAI or Google Gemini), created for this build with a spending limit.
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
Intake verified; API documentation, sandbox and cloud access confirmed.
Weeks 1–2
Tool design document agreed, then read tools and the server built.
Week 3
Sign-in, write actions with confirmation, rate limits and action log added.
Weeks 3–4
Twenty agreed tasks tested in two AI assistants, results in a test report.
Week 4
Your review and two rounds of changes.
Go-live
Server deployed to your cloud, recorded handoff, support starts.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $3,900 |
| Express | 15–25 working days | $4,600 |
| Rush | 10–20 working days | $5,250 |
What you receive at handover
- A tool design document for up to eight tools
- One remote MCP server deployed to your own cloud account
- OAuth or scoped API key sign-in, up to three confirmed write actions, rate limiting and action logging
- A test report for twenty tasks in two AI assistants
- A developer README, a user setup guide and the recorded handoff session
- 30 days of post-launch support
Expected benefits and how to measure them
- Can let customers use your product from the AI assistants they already have
- Helps internal teams find answers in your system without switching tools
- Keeps a full record of what assistants did through the server
- Tool calls per week
- Read weekly call counts from the server's action log after launch, and watch which tools are used most.
- Accounts or users connected
- Count distinct signed-in users or customer accounts in the action log each month after launch.
- Integration requests from customers
- Count support or sales requests about AI assistant support in the quarter before launch and the quarter after.
Before you start: Before work starts, note how often customers or staff ask for AI assistant access and which tasks they mention most.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
The build is paid once. Running it means cloud hosting for one small server in your own account.
The AI assistants themselves are paid by whoever uses them, under their own plans. We charge nothing per tool call.
Ask about running costsCustom requirements
A second system, more than eight tools, ChatGPT app UI components or enterprise SSO beyond OAuth is outside this build. Add a note at checkout for a written Change Quote. Larger or bespoke projects go through the Custom AI enquiry form.
Request a custom versionKeep it running
After the 30 days of post-launch support, optional Upmark maintenance covers monitoring, fixes, protocol and dependency updates, and a monthly health report. Its change hours suit small changes, such as adjusting a tool when your API changes. Cancel any month.
$249/mo (the minimum fee, includes 2 change hours)
Optional. Starts after the post-launch support period. Cancel any month.
How maintenance worksHow this compares
| Do it yourself with a SaaS tool | Hire a custom agency | Upmark Labs | |
|---|---|---|---|
| Price | A connector platform usually bills monthly by usage or seats | A development agency quotes after discovery | Ours is one fixed price, shown before you buy |
| Tools | Generic actions the platform already supports | Agreed during the project | Ours designs up to eight tools around your own system |
| Write safety | Depends on the platform's settings | Depends on the build | Ours is read-only by default, with confirmed write actions |
| Hosting | Runs in the vendor's cloud | Depends on the contract | Ours is deployed to your own cloud account |
Questions about this product
What is an MCP server, in plain terms?
It is a small service that lets AI assistants use your system through clearly defined tools. MCP stands for Model Context Protocol, an open standard many assistants support. Instead of each assistant needing its own integration, they connect to one server that controls what they can see and do.
Which AI assistants is the MCP server tested with?
Two MCP-capable assistants, such as ChatGPT and Microsoft Copilot, agreed at intake. We run twenty agreed tasks in each and record the results in a test report. Other assistants that support remote MCP servers can usually connect too. Only the two agreed assistants are tested in the build.
Can AI assistants change data through the server?
Only where you allow it. Tools are read-only by default. Up to three write actions can be added, each with a confirmation step and your security sign-off before it is built. Every action is logged, and rate limits stop an assistant from making too many calls at once.
How do users sign in to the MCP server?
Through OAuth or scoped API keys, chosen during tool design. OAuth lets each user sign in with their own account, so the assistant only sees what that user may see. Enterprise single sign-on beyond OAuth is outside this build and needs a Change Quote.
Will the server be listed in AI assistant app directories?
Not as part of this build. We provide submission support, such as the details a directory usually asks for, but the listing decision belongs to each directory. Approval is never certain. The server works without a listing for your own team and for customers you give the connection details to.
Do we need to change our own API first?
Usually not. The server sits on top of your existing API or database. Your API needs to be stable, with authentication in place. We do not change your API as part of this build. If a tool needs an endpoint you do not have yet, we flag it during tool design.
What does it cost to host the MCP server?
Cloud hosting for one small server in your own account, such as AWS, Google Cloud, Azure or Cloudflare. The cost depends on your provider and how often assistants call the tools. We charge nothing per call. Optional monthly maintenance is available after the support period for updates and fixes.
Give AI assistants a safe, logged way into your system.
Order the MCP Server Build and your product or data becomes usable from AI assistants. You control the tools and sign-in, and every action is on record.
Works well with
