CustomMCPServer

The CustomMCPServer project demonstrates how to build a lightweight, Python‑based Model Context Protocol (MCP) server that can be plugged directly into an AI assistant such as Claude. By exposing data and functionality through MCP’s standardized interfaces, developers can separate the concerns of context provisioning from LLM interaction. This approach lets teams create reusable, secure services that feed contextual information into the model while also offering executable tools that the assistant can invoke on demand.

At its core, the server solves a common pain point: integrating external data sources and custom logic into an LLM‑driven workflow without rewriting the assistant’s core code. Instead of hardcoding database queries or API calls into the LLM prompt, developers expose them as Resources (read‑only endpoints that populate the model’s context) and Tools (actionable commands that perform side effects). This modularity allows the assistant to request fresh data or trigger operations in a controlled, authenticated manner, while keeping the LLM’s reasoning separate from implementation details.

Key capabilities of this MCP server include:

• Resource provisioning – Define simple GET‑style endpoints that load structured data (e.g., user profiles, product catalogs) into the model’s context.
• Tool execution – Expose POST‑style actions such as inserting records, sending emails, or running custom scripts that the assistant can call from within a conversation.
• Prompt templates – Reusable prompt fragments that standardize how the assistant formats requests to the server, ensuring consistent interaction patterns.
• CLI integration – A command‑line interface that installs the server into Claude’s configuration, automatically updating the assistant’s tool list and making the new services immediately available.

Real‑world scenarios for this server span from automating database seeding (e.g., generating thousands of mock users) to integrating with enterprise APIs for real‑time data retrieval. For example, a product manager could ask the assistant to “list all active customers who purchased in the last month,” and the server would fetch that data via a Resource, while a tool could be used to “send a promotional email” directly from the assistant’s conversation. This tight coupling between context and action streamlines workflows, reduces manual copy‑paste steps, and keeps sensitive logic isolated from the LLM.

Because MCP is designed to be language‑agnostic, the CustomMCPServer can serve any LLM client that supports the protocol. Developers can extend the server with additional resources or tools—such as connecting to external databases, invoking machine learning models, or orchestrating microservices—without touching the assistant’s core code. The result is a flexible, secure bridge that empowers AI assistants to become true agents capable of reading from and writing to the world around them.