AI Agents · Model Context Protocol Development
MCP Server Development
MCP development is building Model Context Protocol servers: small services that expose your systems and data to AI assistants and agents as a standard set of tools and resources. Aaga designs and builds custom MCP servers with tight permissions, authentication, logging and tests, and runs its own MCP server, aaga-mcp, for the Aaga platform.
- Connect AI agents to your CRM, ERP, databases, documents and internal APIs once
- Reuse the same integration across Claude, other MCP clients and your own agents
- Scope every tool tightly, with authentication, deny lists and approval on risky actions
- Test, log and version your MCP servers like any other production service
- aaga-mcp connects MCP clients to the Aaga platform
- Own MCP server
- Start with one system and a few tools
- Pilot first
- Reply to your inquiry within one business day
- 1 day
What Kinds of MCP Servers Do We Build?
Each server exposes only what agents need, in a form they can use reliably.
CRM and Sales Systems
Look up accounts and contacts, log activities, create leads and update deal stages, within the permissions you set.
ERP and Operations
Read orders, stock and invoices, and prepare changes for a person to approve.
Databases and Warehouses
Safe, read-only query tools and curated views instead of open access to production data.
Documents and Knowledge
Search and fetch documents as MCP resources, with access control per user.
Internal APIs and Tools
Wrap your own services, ticketing and admin tools as typed MCP tools with clear descriptions.
Platform and Product MCP
An MCP server for your own SaaS product, so your customers can use it from their AI assistants.
How an MCP Development Project Runs
Pick the System and Tasks
We agree which system to expose and which tasks agents should be able to do in it.
Design Tools and Permissions
We define each tool's name, description, inputs and outputs, plus who may call it and what needs approval.
Build and Secure
We build the server with authentication, input validation, rate limits, deny lists and logging.
Test With Real Agents
We test the server with MCP clients and agents on real tasks, and fix tools the model misuses.
Deploy and Monitor
We deploy locally or as a remote service, with versioning, monitoring and documentation.
MCP Server vs Custom Integration per Agent
Both work. MCP pays off when more than one AI application needs the same system.
| MCP server | Custom integration per agent | |
|---|---|---|
| Reuse | One server, many MCP-compatible clients and agents | Rebuilt for each assistant or framework |
| Changing models | Swap models or clients without touching the integration | Integration often tied to one model's tool format |
| Permissions | Defined once, at the server | Repeated, and easy to get inconsistent |
| Discovery | Clients list available tools and resources at runtime | Tools hard-coded into each application |
| Best for | Systems many agents or teams will use | A single, narrow integration in one app |
Comparison describes typical integration approaches, not any specific product.
What Is MCP Development?
MCP development is the work of building servers that implement the Model Context Protocol (MCP), an open standard for connecting AI applications to tools and data. An MCP server wraps a system, such as your CRM, ERP, database or internal API, and offers it to AI assistants and agents as a set of well-described tools and resources. Once a system has an MCP server, any MCP-compatible client can use it, so you build the integration once instead of once per AI tool.
For a plain-language introduction to the protocol itself, read our guide to the Model Context Protocol explained for business teams. This page covers how we design and build MCP servers for production use.
Why Are Companies Building MCP Servers?
AI agents are only as useful as the systems they can reach. Without a standard, every assistant, framework and model needs its own integration, and each one has to repeat the same permission and security work. MCP changes that:
- Build once, use many times. The same server works with different MCP clients and agents.
- Change models freely. Switching model provider doesn't mean rewriting integrations.
- Central control. Permissions, logging and limits live in one place, at the server.
- Discovery. Clients ask the server which tools exist, so new capabilities appear without code changes in every client.
MCP is most valuable for systems that several teams or agents will use. That is why it sits at the heart of much of our AI agent development work.
What Does an MCP Server Expose?
An MCP server offers three kinds of building blocks:
| Building block | What it is | Example |
|---|---|---|
| Tools | Actions the AI can call, with typed inputs and outputs | create_lead, get_order_status, draft_invoice |
| Resources | Data the AI can read, identified by a URI | A customer record, a policy document, a report |
| Prompts | Reusable templates a user or client can pick | "Summarize this account", "Prepare a renewal brief" |
Most of the design effort goes into tools. A model chooses tools from their names and descriptions, so vague or overlapping tools lead to wrong calls. We keep each tool focused on one task, write descriptions that say when to use it and when not to, validate every input and return results the model can act on.
How Do We Keep MCP Servers Secure?
An MCP server gives an AI system real access to real systems, so we treat it like any other production service, with extra care for AI-specific risks:
- Least privilege. Each server, and each client, gets only the tools and data it needs. Read-only by default.
- Authentication. Every client is identified, through API keys for internal and local servers or OAuth-based authorization for remote ones.
- Deny lists and safe defaults. High-risk tools can be switched off per deployment, and destructive actions such as deletes stay off unless explicitly enabled.
- Human approval. Sensitive actions, such as sending a payment or a customer email, return a draft for a person to approve.
- Prompt-injection defenses. Content from emails, documents and web pages is treated as data, never as instructions, and tool results are checked before they trigger further actions.
- Logging. Every call is logged with who called it, with what inputs and what happened.
Our application security practice reviews servers that touch sensitive data, and our AI security checklist lists the controls we check on every AI project.
aaga-mcp: Our Own MCP Server
We use MCP in our own product. aaga-mcp is the MCP server for the Aaga platform, our no-code and low-code platform for modules, workflows, permissions, integrations and AI agents. It connects MCP clients such as Claude Code and Claude Desktop to the platform's backend, so an AI assistant can work with an Aaga workspace through tools rather than screens.
A few design choices from aaga-mcp that we bring to client projects:
- API-key authentication. Each request to the backend carries a key issued to a dedicated API user, shown once when it is created.
- Dynamic tools. The server can ask the backend which tools exist and build its tool list from the answer, so new platform capabilities appear without a server release.
- A deny list. Selected tools, such as creating new modules, can be blocked per installation.
- Deletes off by default. Destructive actions are only available when an administrator explicitly enables them.
The platform behind it is described in our no-code platform case study.
How Do You Test an MCP Server?
An MCP server can be correct and still be used badly by a model. We test at two levels:
- As software. Unit and integration tests for every tool, including permission checks, invalid inputs and error handling. Tools such as the MCP Inspector help check the server against the protocol.
- With real agents. We run realistic tasks through one or more MCP clients and check which tools the model picks, with which inputs, and whether the task completes. When a model keeps misusing a tool, we fix the tool's name, description or shape, not just the prompt.
The task suite becomes a regression test, run whenever a tool, model or client changes.
Local or Remote MCP Servers?
MCP supports servers that run on the user's machine, launched by the client and talking over standard input and output (stdio), and remote servers reached over HTTP. Local servers suit developer tools and desktop assistants. Remote servers suit shared business systems, product MCP servers for your customers and agents running in the cloud. We pick the transport, hosting and authentication model based on who will use the server and where your data must stay.
Where Does MCP Fit in Your AI Plans?
MCP is a building block, not a project on its own. It pays off when you know which agents or assistants will use it and for what. A typical path: pick one workflow, decide which system the agent needs, build an MCP server for that system with a handful of tools, and add tools as more use cases arrive. If you are still deciding where agents fit, our AI consulting team can help you choose the first workflow, and our explainer on agentic AI covers the concepts.
Why Aaga for MCP Development?
Aaga is an AI-native engineering company with 100+ clients across the USA, Canada, the UK, the Netherlands, Dubai (UAE) and India. We build MCP servers as part of real agent projects, we run our own MCP server for our platform, and we treat permissions, testing and logging as core engineering. You work directly with the senior engineers who build your server, and you own the code.
Frequently Asked Questions
An MCP server is a service that exposes a system's capabilities to AI applications using the Model Context Protocol. It offers tools (actions the AI can call), resources (data it can read) and prompts (reusable templates). Any MCP-compatible client, such as an AI assistant or agent, can discover and use them.
MCP development services cover designing, building, securing, testing and running MCP servers for your systems. That includes choosing which tools and resources to expose, writing clear tool descriptions, adding authentication and permissions, and testing the server with real AI clients.
When more than one AI application will use the same system, or when you want to switch models and clients without rebuilding integrations. For a single, narrow integration inside one app, a direct API call can be simpler, and we will tell you if that is the better choice.
MCP is a protocol; security depends on how the server is built. We give each server the least access it needs, authenticate every client, validate inputs, keep destructive actions off or behind approval, defend against prompt injection and log every call.
Yes. A product MCP server lets your customers use your product from their AI assistants and agents. We design the tool set around your public API and permission model, and add authentication so each customer only reaches their own data.
Yes. Aaga runs its own MCP server, aaga-mcp, which connects MCP clients such as Claude Code and Claude Desktop to the Aaga platform. It authenticates with an API key, can load its tool list from the platform, blocks selected tools through a deny list and keeps deletes off unless they are explicitly enabled.
We usually build MCP servers with the official TypeScript or Python SDKs. Local servers typically run over stdio, launched by the client; remote servers run over HTTP with proper authentication. We choose based on who will use the server and where it needs to run.
Give Your AI Agents Safe Access to Your Systems
Pick one system. We'll design the tools, permissions and tests, and build an MCP server your agents can rely on.
Plan My MCP Server