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    A Flask-based MCP server designed for testing deployment on Google App Engine. Provides a deeplink checking endpoint that accepts flattened JSON parameters and forwards them as nested objects to external APIs.
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    Enables AI agents to validate wireless communication ideas by providing standardized channel instances and physical measurements through MCP, eliminating the need to write simulations from scratch.
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    A Python MCP server implemented from scratch, providing tool execution capabilities like system time and file hashing via stdio or HTTP transport.
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    Generates crash-proof Python LLM pipelines from plain English descriptions. Pipelines resume from the last successful node after a crash instead of restarting from scratch. IProvider-agnostic , works with Groq, Gemini, Ollama, OpenAI, or any Python callable. Zero infrastructure, runs entirely on free-tier APIs.
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    MCP server providing persistent memory and context for AI tools, including semantic memory, knowledge graph, and session history to avoid starting from scratch in every conversation.
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    A demonstration MCP server providing basic tools for returning greetings and performing mathematical addition. It serves as a practical workshop guide for setting up and configuring Model Context Protocol servers from scratch using Python.
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    A local MCP server that guides users through Linux From Scratch (LFS) documentation step by step, tracking progress in a SQLite database and providing search, sequential navigation, and optional AI chat assistance.
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    Connects to Salesforce scratch orgs and allows AI to generate and insert realistic nested test records via natural language descriptions using the Composite Tree REST API.
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