Function-Calling
PulseAugur coverage of Function-Calling — every cluster mentioning Function-Calling across labs, papers, and developer communities, ranked by signal.
5 day(s) with sentiment data
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Model Context Protocol (MCP) shifts AI agent interaction from APIs to self-describing tools
The Model Context Protocol (MCP) is a new approach that changes how AI models interact with APIs. Unlike traditional APIs, which require developers to explicitly instruct models on how to use each tool, MCP allows tools…
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LLM structured output guarantees syntax, not semantics, leading to 'well-formed lies'
Structured output for LLMs, such as JSON schema enforcement, guarantees that the generated text is syntactically correct but does not ensure semantic accuracy. While these tools eliminate parsing errors, they can lead t…
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MCP vs Function Calling: Latency and code size benchmarks favor MCP for scaled tool reuse
A data-driven comparison between MCP and Function Calling for tool reuse in AI agents reveals that while MCP incurs a 2ms communication overhead per call, this is negligible for typical agent tasks where LLM inference d…
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MCP protocol centralizes AI agent tool integration, reducing code duplication
The article introduces MCP (Model Context Protocol), a new system designed to streamline the integration of AI agents with external tools like Jira. Unlike traditional function calling, where tool definitions and execut…
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Google's A2A protocol finds niche in agent-to-agent communication
Google's Agent2Agent (A2A) protocol, introduced in April 2025, aims to standardize communication between independent AI agents from various vendors and frameworks. Initially met with skepticism due to market saturation …
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AI Function Calling Tutorial: DeepSeek V4-Pro Integrates with APIs
This tutorial demonstrates how to implement function calling in AI models, enabling them to interact with external APIs. It provides a practical example using the DeepSeek V4-Pro model to call a weather API based on use…
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Developer builds agentic RAG system from scratch using Python and minsearch
A developer detailed their experience building an agentic RAG system from scratch as part of the LLM Zoomcamp 2026. The process involved creating a retrieval-augmented generation pipeline using Python and a lightweight…
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LLM function calling explained: How models use tools and avoid errors
This article explains function calling, a key capability for LLMs to interact with external tools and data. It details how models decide which tool to use and with what arguments, moving beyond simple text prediction to…
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Developer clarifies RAG, Function Calling, MCP, and Semantic Kernel
This article details a developer's journey in understanding and applying Retrieval-Augmented Generation (RAG), Function Calling, MCP, and Semantic Kernel within .NET AI applications. It aims to clarify when to utilize e…
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MCP Protocol Emerges as Distinct AI Communication Standard
The author initially believed that MCP (Model Communication Protocol) was simply another term for function calling in AI models. However, upon further investigation, they discovered that MCP is a distinct protocol desig…
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AI agents: MCP vs. Function Calling for external world interaction
The article explores how AI agents can interact with the external world, focusing on two primary methods: MCP (Multi-Agent Communication Protocol) and function calling. It highlights that tools are essential for agents …
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LLM Integration Guide: MCP, Tool Use, and Function Calling Explained
This article explores three distinct approaches for integrating large language models (LLMs) with external systems: MCP, tool use, and function calling. It aims to clarify the differences between these architectures and…
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LLM research tackles uncertainty in function calls and system propagation
Two new research papers explore the critical issue of uncertainty in Large Language Models (LLMs). The first paper investigates uncertainty quantification methods specifically for LLM function-calling, finding that simp…