Overview

This MCP server bridges the Model Context Protocol (MCP) ecosystem with Microsoft’s Semantic Kernel, providing developers a ready‑to‑use framework for turning MCP tools into Semantic Kernel functions. By exposing MCP capabilities through a local .NET server, the solution eliminates the need for external Node.js dependencies while still allowing optional integration with cloud‑based MCP services such as GitHub or Everything. The primary value lies in enabling large language models to perform function calls that interact with real‑world data sources, all within a single, extensible runtime.

What Problem Does It Solve?

Modern AI assistants frequently need to reach beyond the model’s internal knowledge base, invoking external APIs or querying databases. MCP standardizes this interaction, but developers still face friction when integrating MCP tools into their preferred AI frameworks. The “MCP with Semantic Kernel” server resolves this friction by automatically converting MCP tool definitions into Semantic Kernel function descriptors, streamlining the workflow for developers who already rely on Semantic Kernel’s prompt orchestration and LLM binding.

Core Functionality & Value

• Automatic MCP Server Discovery – The client connects to all enabled servers (local or remote) defined in configuration, retrieving tool catalogs without manual registration.
• Tool Listing & Execution – Developers can list available tools and invoke them via simple CLI commands, with the server handling serialization of arguments and results.
• Semantic Kernel Integration – Tools are exposed as Semantic Kernel functions, allowing LLMs to call them through function‑calling prompts. This gives models direct access to external logic while maintaining the declarative style of Semantic Kernel.
• Extensible Architecture – The server is written in .NET 9 and uses the NuGet package, making it straightforward to add new MCP servers or extend existing ones with custom logic.

Use Cases & Real‑World Scenarios

• Hybrid Retrieval – An LLM can query a corporate knowledge base via an MCP “Search” tool, then use Semantic Kernel to compose a response that blends retrieved facts with generative text.
• Automation Pipelines – Developers can chain MCP tools (e.g., “GetDateTime”, “Add”) into Semantic Kernel workflows to build automated assistants that perform calculations or schedule events.
• Rapid Prototyping – The local MCP server ships with a suite of testing tools (Echo, Calculator, DateTime) that let developers experiment with function calling before connecting to production services.
• Cross‑Platform Integration – By enabling external MCP servers like GitHub, the same client can invoke repository queries or issue creation directly from an LLM prompt.

Integration into AI Workflows

The server’s design aligns with typical AI development cycles:

1. Define MCP Tools – Create or consume tools via the MCP specification.
2. Run Local Server – Start the .NET MCP server to expose those tools.
3. Configure Semantic Kernel – Bind an LLM and register the MCP‑derived functions as plugins.
4. Invoke from Prompts – Allow the model to call functions during generation, with Semantic Kernel handling the orchestration and result injection.

Because the server abstracts away protocol details, developers can focus on crafting prompts and workflows rather than managing low‑level communication.

Unique Advantages

• Zero Node.js Dependency for Local Use – Developers who prefer a pure .NET stack can run the entire solution locally, avoiding external runtime requirements.
• Unified Tool Representation – MCP tools are automatically translated into Semantic Kernel function descriptors, ensuring consistency across different AI engines that support the same protocol.
• Interactive CLI for Quick Testing – The included client offers a command‑line interface to explore tool catalogs and perform sample calls, accelerating debugging and experimentation.
• Extensible Configuration – The file allows toggling between local and remote MCP servers without code changes, making it simple to switch environments.

Overall, the “MCP with Semantic Kernel” server provides a seamless pathway for AI developers to enrich language models with external capabilities, combining the flexibility of MCP with the expressive power of Semantic Kernel.