AI / ML
MCP server implementation
I build and deploy a turnkey MCP server so AI models can work safely with your data and systems — not just answer, but take real actions.
What is MCP server
MCP (Model Context Protocol) is a standard that lets AI models work securely with your data, services, and business processes through a single set of tools. Thanks to it, an AI assistant doesn't just answer questions but performs real actions: it creates tasks, writes data, retrieves information from systems, and triggers processes. The MCP server becomes the intermediary between the model and your systems — CRM, databases, internal APIs. Access to each tool is described explicitly, so the model acts only within what you've allowed.
Who it's for
What's included
Integration with CRM, databases, APIs, and services
I connect the MCP server to your systems: CRM, databases, internal and external APIs, corporate services. The AI retrieves information and takes actions right where your business runs.
Connecting AI models
I set up work with ChatGPT, Claude, Gemini, Yandex AI, and other models. If needed, I connect your own LLMs or in-house solutions when data can't leave your perimeter.
Tools for taking actions
I define the set of tools the model acts through: it creates tasks, writes data, retrieves information from systems, and triggers business processes. Each tool is scoped explicitly — the model does only what's allowed.
Authorization and secure access
I set up authentication, access control, and permission limits at the tool level. Connections to systems run over secure channels, and the model's actions stay within the defined boundaries.
Error alerts to messengers
I add alerts to Telegram or another messenger: you learn about integration failures, tool errors, and unavailable systems right away — not from your users.
Documentation and deployment
I prepare documentation for launch and use, deploy the MCP server on your server, and hand it over ready to run. You remain the owner of the code, data, and infrastructure.
How it works
- 01
Goals and list of tasks
I discuss what the MCP server should do, which actions are needed, and where it will be used. I capture the scenarios and the priority set of tools to start with.
- 02
Designing integrations and access
I design the integrations with your systems, the set of tools, and the access model. I decide up front what permissions the model gets and where the boundaries of its actions lie.
- 03
Implementation and connecting systems
I build the MCP server and connect your CRM, databases, APIs, and internal services. The tools return data and perform actions in real systems.
- 04
Connecting AI models
I connect the chosen models — ChatGPT, Claude, Gemini, Yandex AI, or your own LLMs. I verify that the model calls the tools correctly and understands what they're for.
- 05
Authorization, security, and alerts
I set up authorization, permission limits, and secure connections, and add error alerts to a messenger. The model's actions stay under control, and failures don't go unnoticed.
- 06
Testing, documentation, deployment
I test the tools and scenarios, prepare documentation, and deploy the server on your infrastructure. After that I keep supporting it and expand the set of tools as new tasks come up.
MCP server architecture
AI models call the MCP server, which connects to your systems — CRM, ERP, databases, and APIs — through a set of tools and an authorization layer. Errors go out as alerts to a messenger, and the server itself is deployed on the customer's infrastructure.
AI models
ChatGPT, Claude, Gemini, Yandex AI, or your own LLMs. The model forms an intent and calls the right tool through MCP rather than accessing the systems directly.
MCP server
The core of the solution: the set of tools, processing of the model's requests, and routing of calls to systems. Each tool is described explicitly, so the model acts predictably and within the defined boundaries.
Integrations
Connections to CRM, ERP, databases, internal and external APIs, Telegram bots, and task trackers. Through them the AI retrieves data and performs real actions in your systems.
Authorization and security
Authentication, access control, and permission limits at the tool level, plus secure connections to systems. The model sees and does only what's allowed.
Notifications and monitoring
Alerts about tool errors, integration failures, and unavailable systems arrive in a messenger. Problems are visible right away, not through user complaints.
Deployment
The MCP server is deployed on the customer's server and comes with documentation for launch and use. The code, data, and infrastructure stay under your control.
Tech stack
Clients
Testimonials
The core value of this service is AI that securely performs actions in your systems, not just answers questions. Through the MCP server, the model creates tasks, writes and retrieves data, triggers business processes, and works with external APIs — within what you’ve explicitly allowed. Authorization, permission limits, and deployment on your server keep access under control, while error alerts to a messenger surface failures right away. As a result, the AI assistant becomes a real participant in your workflows rather than a separate chat window.
Shall we discuss your task?
FAQ
Which AI models can I use the MCP server with? +
With ChatGPT, Claude, Gemini, Yandex AI, and other models that support tool calling. If needed, I connect your own LLMs or in-house solutions — for example, when data can't leave your perimeter. MCP is an open standard, so the choice of model stays flexible, and you can switch it without rewriting the integrations.
Which systems can be connected? +
Practically any system that has an API: CRM and ERP, databases, websites, Telegram bots, task trackers, analytics systems, and internal services. For each system I define a set of tools through which the AI retrieves data and performs actions. If a system has no ready-made API, I work out a way to access it separately.
Can new functionality be added after launch? +
Yes, the architecture is extensible from the start. After launch you can add new services, tools, and AI agents without breaking what already works. I usually begin with a priority set of tools and then expand the scope as new tasks appear.
Is MCP suitable for automating business processes? +
Yes, that's one of the main use cases. On top of an MCP server you can build AI assistants for working with customers, handling requests, creating tasks, and triggering processes across several systems at once. The AI doesn't just answer — it performs actions and ties disparate tools into a single workflow.
What is MCP and how does it differ from a regular API integration? +
MCP (Model Context Protocol) is a standard protocol that gives an AI model secure access to tools and data. Unlike a one-off connector built for a single task, MCP describes tools in a uniform way, so the model understands what it can do and how. This makes it easier to add new systems and switch models — the integrations stay reusable rather than written for a single case.
Is it safe to give AI access to systems? +
Access is built around authorization, access control, and permission limits at the level of each tool — the model does only what you've allowed. Connections to systems run over secure channels, and the MCP server itself is deployed on your server, so data never leaves your perimeter. Errors and suspicious behavior are visible through alerts in a messenger.
How long does implementation take? +
The timeline depends on the number of systems being connected and the set of tools. I start with a priority scenario, launch it, and then expand the scope — so the first results appear before everything is fully ready. I don't quote hard deadlines in days: the scope is determined by the set of integrations, which we lock in at the start.