This article details a schema-grounded approach to Conversational User Interface (CUI) development using OpenCUI, focusing on declaring schemas, attaching interaction and language annotations, and leveraging statecharts for efficient dialog management. It emphasizes building CUIs around backend service APIs.
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.
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 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.
This article discusses using entropy and variance of entropy (VarEntropy) to detect hallucinations in LLM function calling, focusing on how structured outputs allow for identifying errors through statistical anomalies in token confidence.
Huginn is presented as a robust, open-source alternative to IFTTT, offering greater customization, privacy through self-hosting, and the ability to handle complex workflows with API integrations. While it requires more technical expertise than IFTTT, it provides significantly more power and control.
This Space demonstrates a simple method for embedding text using a LLM (Large Language Model) via the Hugging Face Inference API. It showcases how to convert text into numerical vector representations, useful for semantic search and similarity comparisons.
This article explores the Model Context Protocol (MCP), an open protocol designed to standardize AI interaction with tools and data, addressing the fragmentation in AI agent ecosystems. It details current use cases, future possibilities, and challenges in adopting MCP.
This document details how to use function calling with Mistral AI models to connect to external tools and build more complex applications, outlining a four-step process: User query & tool specification, Model argument generation, User function execution, and Model final answer generation.
The Gemini API documentation provides comprehensive information about Google's Gemini models and their capabilities. It includes guides on generating content with Gemini models, native image generation, long context exploration, and generating structured outputs. The documentation offers examples in Python, Node.js, and REST for using the Gemini API, covering various applications like text and image generation, and integrating Gemini in Google AI Studio.
Model Context Protocol (MCP) is a bridging technology for AI agents and APIs. It standardizes API access for AI agents, making it a universal method for AI agents to trigger external actions.
