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Run and debug C# MCP servers locally. Covers IDE configuration, MCP Inspector testing, GitHub Copilot Agent Mode integration, logging setup, and troubleshooting. USE FOR: running MCP servers locally with dotnet run, configuring VS Code or Visual Studio for MCP debugging, testing tools with MCP Inspector, testing with GitHub Copilot Agent Mode, diagnosing tool registration issues, setting up mcp.json configuration, debugging MCP protocol messages, configuring logging for stdio and HTTP servers. DO NOT USE FOR: creating new MCP servers (use mcp-csharp-create), writing automated tests (use mcp-csharp-test), publishing or deploying to production (use mcp-csharp-publish).

Publish and deploy C# MCP servers. Covers NuGet packaging for stdio servers, Docker containerization for HTTP servers, Azure Container Apps and App Service deployment, and publishing to the official MCP Registry. USE FOR: packaging stdio MCP servers as NuGet tools, creating Dockerfiles for HTTP MCP servers, deploying to Azure Container Apps or App Service, publishing to the MCP Registry at registry.modelcontextprotocol.io, configuring server.json for MCP package metadata, setting up CI/CD for MCP server publishing. DO NOT USE FOR: publishing general NuGet libraries (not MCP-specific), general Docker guidance unrelated to MCP, creating new servers (use mcp-csharp-create), debugging (use mcp-csharp-debug), writing tests (use mcp-csharp-test).

Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).

Test C# MCP servers at multiple levels: unit tests for individual tools and integration tests using the MCP client SDK. USE FOR: unit testing MCP tool methods, integration testing with in-memory MCP client/server, end-to-end testing via MCP protocol, testing HTTP MCP servers with WebApplicationFactory, mocking dependencies in tool tests, creating evaluations for MCP servers, writing eval questions, measuring tool quality. DO NOT USE FOR: testing MCP clients (this is server testing only), load or performance testing, testing non-.NET MCP servers, debugging server issues (use mcp-csharp-debug).

Create MCP servers using the C# SDK and .NET project templates. Covers scaffolding, tool/prompt/resource implementation, and transport configuration for stdio and HTTP. USE FOR: creating new MCP server projects, scaffolding with dotnet new mcpserver, adding MCP tools/prompts/resources, choosing stdio vs HTTP transport, configuring MCP hosting in Program.cs, setting up ASP.NET Core MCP endpoints with MapMcp. DO NOT USE FOR: debugging or running existing servers (use mcp-csharp-debug), writing tests (use mcp-csharp-test), publishing or deploying (use mcp-csharp-publish), building MCP clients, non-.NET MCP servers.

Convert files and office documents to Markdown with support for 15+ formats and AI-enhanced features.

Create professional infographics using Nano Banana Pro AI with smart iterative refinement and quality review.

A curated index of scientific datasets, models, and demos for AI/ML research in various scientific domains.

Generate or edit images using AI models for various visual content needs.

Structured workflow for collaboratively authoring documentation, proposals, specs, and similar content. \n \n Guides users through three stages: Context Gathering (closing knowledge gaps), Refinement & Structure (building sections iteratively), and Reader Testing (validating the doc works for fresh readers) \n Supports integration with shared documents, team channels, and templates to pull in context directly when connectors are available \n Uses brainstorming and curation cycles for each s