Simple MCP Server with Python – Overview

The Simple MCP Server is a lightweight, reference implementation that demonstrates how to expose data, functionality, and interaction templates to large language models using the Model Context Protocol (MCP). By leveraging the MCP Python SDK, developers can create a modular server that cleanly separates context delivery (resources), action execution (tools), and conversational scaffolding (prompts). This separation allows AI assistants to consume structured information, perform external operations, and follow predefined dialogue flows without entangling business logic with model internals.

At its core, the server solves the problem of context friction—the difficulty of providing LLMs with up‑to‑date, authoritative data while keeping the model stateless. Resources act as read‑only endpoints that can be queried by the LLM for facts, configuration, or file contents. Tools provide a secure execution surface where the model can request external actions such as API calls, database updates, or computational routines. Prompts give developers a way to predefine conversational patterns (e.g., slash commands or menu selections) that the model can trigger, ensuring consistent user experiences and reducing hallucinations. Together, these primitives enable developers to build AI assistants that behave predictably, respect data governance policies, and integrate seamlessly with existing infrastructure.

Key capabilities of the server include:

• Dynamic capability advertising: On startup, the server declares support for , , and along with flags like and . Clients can discover available primitives at runtime, enabling adaptive UI components or fallback logic when features are unavailable.
• Live resource updates: With the flag, clients can register for real‑time notifications when resource data changes. This is ideal for dashboards or collaborative tools where the model must reflect the latest state without polling.
• Model‑controlled tool execution: Tools are exposed to the LLM with a simple declarative interface. The model can invoke these tools by name, passing arguments that the server validates before execution, thus preventing arbitrary code execution and maintaining security boundaries.
• Prompt templating: Prompts can be defined once and reused across conversations, allowing developers to embed complex interaction logic (e.g., confirmation steps or multi‑turn workflows) directly into the model’s context.

Typical use cases span from knowledge‑base chatbots that need to fetch the latest policy documents (resources) and perform CRUD operations on a backend (tools), to interactive coding assistants that provide templated prompts for debugging patterns. In a DevOps scenario, the server could expose CI/CD pipeline triggers as tools while delivering repository metadata as resources, letting a model orchestrate deployments without direct access to the underlying systems.

Integration into AI workflows is straightforward: an LLM client sends a request to the MCP server, receives a list of available primitives, and then interacts using the defined protocols. Because the server adheres to the MCP specification, any compliant client—whether a web UI, a command‑line tool, or another microservice—can communicate without custom adapters. This plug‑and‑play nature reduces integration effort and promotes a consistent developer experience across heterogeneous AI applications.