This article details the development and implementation of an MCP (Multi-Modal Conversation Protocol) for scheduling social media posts within the Postiz open-source social media scheduling tool. It discusses the challenges of using SSE for transport and the benefits of WebSockets, as well as techniques for forcing LLMs to execute necessary configuration steps before scheduling. It highlights the use of decorators for creating API endpoints and the potential for integrating Postiz with other tools like Cursor and Notion.
This article provides a hands-on guide to Anthropic’s Model Context Protocol (MCP), an open protocol designed to standardize connections between AI systems and data sources. It covers how to set up and use MCP with Claude Desktop and Open WebUI, along with potential challenges and future developments.
This tutorial details how to use FastAPI-MCP to convert a FastAPI endpoint (fetching US National Park alerts) into an MCP-compatible server. It covers environment setup, app creation, testing, and MCP server implementation with Cursor IDE.
Researchers from AWS and Intuit have designed a zero-trust security framework for the Model Context Protocol (MCP), addressing threats like tool poisoning and unauthorized access through multi-layered defenses including Just-in-Time access control and behavior-based monitoring.
This article details the author's insights into AI function calling, its challenges, and the Agentica framework developed to address them, emphasizing the importance of JSON schema understanding, compiler support, and a document-driven approach.
This article details a comparison between Model Context Protocol (MCP) and Function Calling, two methods for integrating Large Language Models (LLMs) with external systems. It covers their architectures, security models, scalability, and suitable use cases, highlighting the strengths and weaknesses of each approach.
MCP is best suited for robust, complex applications within secure enterprise environments, while Function Calling excels in straightforward, dynamic task execution scenarios. The choice depends on the specific needs, security requirements, scalability needs, and resource availability of the project.
| MCP Server | Key Functionality | Link to Repository |
|---|---|---|
| Filesystem | Read/Write/Manage files & directories | https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem |
| Google Drive | Search & Access files in Google Drive | https://github.com/modelcontextprotocol/servers/tree/main/src/gdrive |
| Slack | Interact with Slack workspaces (messages, channels, users) | https://github.com/modelcontextprotocol/servers/tree/main/src/slack |
| Memory | Store & Retrieve contextual data (entities, relations) | https://github.com/modelcontextprotocol/servers/tree/main/src/memory |
| Spotify | Control Spotify playback & access content | https://github.com/varunneal/spotify-mcp |
| Notion | Manage to-do lists in Notion | https://github.com/danhilse/notion_mcp |
| Send & Manage emails with attachments | https://github.com/Shy2593666979/mcp-server-email | |
| Windows Control | Programmatically control Windows system operations | https://github.com/Cheffromspace/MCPContro |
| Excel | Manipulate Excel files (create, read, write, format) | https://github.com/haris-musa/excel-mcp-server |
| Fetch | Retrieve & convert web page content to markdown | https://github.com/modelcontextprotocol/servers/tree/main/src/fetch |
This article details six practical use cases for Model Context Protocol (MCP) to automate workflows using AI agents and integrations with tools like Slack, Google Calendar, BigQuery, Linear, GitHub, and HubSpot. It highlights the impact of these automations on team efficiency and productivity.
Model Context Protocol server to run Python code in a sandbox using Pyodide in Deno, isolated from the operating system.
This article details the Model Context Protocol (MCP), a new approach to integrating Large Language Models (LLMs) like Azure OpenAI with tools. MCP focuses on structured data exchange to improve reliability, observability, and functionality, moving beyond simple text-in, text-out interactions. It aims to standardize how LLMs interact with tools, enhancing their ability to utilize those tools effectively.
