In this article I explain step by step how I built a custom MCP server in Golang to expand the capabilities of language models like Claude and how that approach allows integrating your own tools through natural language.
The goal was to demonstrate that an LLM can interact with code and local resources without additional special instructions by creating a tool that lists GitHub repositories and their metadata. The idea is simple and powerful: expose concrete capabilities through the Model Context Protocol so the model can invoke useful functions when it needs them.
What is MCP and why it matters to developers: MCP or Model Context Protocol defines how a capabilities server announces and offers actions that a model can execute. Its key components include a capabilities manifest, an input and output schema, REST or gRPC endpoints for invocations, and client SDKs to facilitate integration from the server and client language. With MCP an LLM does not need complex instructions to use a tool, it simply discovers and calls the available capabilities.
Why I chose Golang: Golang offers lightweight concurrency, simple deployment, and good performance for servers that must handle simultaneous requests from models and applications. Implementing an MCP server in Golang allows managing parallel calls, pagination, and rate limits efficiently, in addition to offering native integrations with most production frameworks and libraries.
Client-server architecture: the MCP server in Golang exposes endpoints that describe capabilities and handle invocations. Client SDKs consume those endpoints and translate requests from the LLM into the format the server expects. In practice, a get_manifest endpoint is usually implemented so the model can discover actions, endpoints to list and execute actions, and hooks for monitoring and logging.
Practical example: GitHub repository listing tool. Instead of giving instructions to the LLM to search for repositories, the MCP server offers a list_repos capability that accepts parameters like owner, page, page_size, and filters. When Claude or any other model needs to see the code or READMEs, it invokes list_repos and receives paginated results in JSON. This makes it easy for the model to navigate local or remote code safely and traceably.
Best practices and production tips: implement cursor-based pagination, apply rate limiting per client and per endpoint, validate inputs and outputs with strict schemas, use JWT or API key-based authentication, encrypt communications with TLS, and log traces for auditing. It is also advisable to design idempotent error handling, retries with exponential backoff, and payload size limits to avoid overload. Caching frequent responses and using streaming when responses are large improves latency and scalability.
Security and compliance: since capabilities can expose sensitive code and data, it is essential to control permissions, audit calls, and segregate environments. Integrating cybersecurity policies and risk analysis is essential for enterprise deployments.
SDKs and extensibility: in addition to the server in Golang, it is common to offer SDKs in Golang and Typescript so that frontend and backend teams can consume the capabilities with a consistent API. The SDKs handle authentication, pagination, and serialization, preventing the model or application from requiring additional logic.
Integration with cloud and BI services: for enterprise environments it is advisable to orchestrate the MCP server with cloud services like AWS or Azure, take advantage of managed services for logging, queues, and databases, and connect outputs to business intelligence systems and Power BI for visualization and analysis. This allows monitoring usage, costs, and performance, and obtaining useful metrics for operations and product.
Why this is relevant for companies: by deploying AI agents that can call concrete capabilities, you can automate complex tasks, accelerate development cycles, and improve user support. Custom applications and custom software benefit from integrating specialized AI agents that use MCP to interact with internal data, code repositories, and cloud services in a controlled and secure way.
Services offered by Q2BSTUDIO: at Q2BSTUDIO we are specialists in software development and custom applications, as well as in artificial intelligence and cybersecurity solutions. We design and implement custom MCP servers, AWS and Azure integrations, data pipelines for business intelligence, AI agents for companies, and dashboards with Power BI. We offer AWS and Azure cloud services, artificial intelligence consulting, implementation of cybersecurity measures, and custom software development to cover specific business needs.
How we can help you: if you need an integration that allows models like Claude to interact with your repositories, databases, or internal services, at Q2BSTUDIO we work from defining the capabilities manifest to secure deployment in production. We implement pagination, rate limiting, authentication, and monitoring, in addition to creating SDKs that simplify its adoption.
Keywords and positioning: custom applications, custom software, artificial intelligence, cybersecurity, AWS and Azure cloud services, business intelligence services, AI for companies, AI agents, and Power BI are areas where Q2BSTUDIO brings practical experience to turn prototypes into robust and scalable systems.
Summary and next steps: building an MCP server in Golang is a practical way to make Claude and other models more useful in business contexts. The recipe includes defining a clear manifest, exposing secure endpoints, offering SDKs, and applying production practices such as pagination, rate limiting, and authentication. If you are looking to develop a custom solution that combines AI agents, integration with repositories, and cloud services, contact Q2BSTUDIO and we will help you design, build, and operate the solution.
Contact Q2BSTUDIO: specialists in software development, custom applications, artificial intelligence, cybersecurity, AWS and Azure cloud services, business intelligence services, AI agents, and Power BI
