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MCP and Tool Calling

Protocols and practices for connecting models to the outside world: updates on the MCP ecosystem, function calling, and toolchain integration.

8 selected items Related topicsAgentsAI CodingOpen-Source Ecosystem

Latest selected stories

Items 1–8 of 8 items
Oct 13Tue
  1. GitHub Blog · AI & ML61

    GitHub Copilot app for Beginners: How to teach Copilot about your codebase

    GitHub released the Copilot app for Beginners tutorial series, covering four ways to give Copilot project context, including .github/copilot-instructions.md custom instructions.

    Why it matters: GitHub's official tutorial lays out four mechanisms for giving Copilot project context and when to use each, so readers can combine them as needed in their own repos.

Sep 26Sat
  1. GitHub Blog · AI & ML64

    GitHub Copilot app for Beginners: How to build custom workflows with canvases

    An official GitHub tutorial shows how to build custom workflows with canvases in the GitHub Copilot app: run the /create-canvas skill in an agent session, describe the workflow, user actions, and agent actions in natural language, and the agent generates boards, checklists, dashboards, and other interfaces that open in the side panel.

    Why it matters: The tutorial covers the three elements of /create-canvas and its two-way collaboration mechanism, so readers can turn their own workflows into reusable canvases.

Sep 25Fri
  1. GitHub Blog · AI & ML68

    AI-powered fuzzing with the GitHub Security Lab Taskflow Agent

    Antonio Morales of GitHub Security Lab has released Fuzzing Taskflow, an autonomous fuzzing pipeline for C/C++ projects built on the Taskflow Agent — you only specify a GitHub repository, and it automatically identifies entry points, analyzes the build system, writes harnesses, runs AFL++, and triages crashes.

    Why it matters: The author explains the design of the automated fuzzing pipeline, showing how LLM agents can take over manual steps like coverage analysis and crash triage.

Jul 2Thu
  1. Mistral AI65

    Leanstral 1.5: Proof Abundance for All

    Mistral released the Apache-2.0 open-source model Leanstral 1.5, with 119B total parameters and 6B activated parameters, trained via mid-training, supervised fine-tuning and CISPO reinforcement learning.

    Why it matters: The official post discloses the full training pipeline, benchmark figures and real code verification cases, letting readers gauge how practical formal verification models are for mathematical proofs and open-source bug finding.

Jun 22Mon
  1. Chrome Developers · AI and Browser68

    A developer toolkit to make your website agent-ready

    Chrome released an agent-ready toolkit for developers, and Lighthouse gains a new Agentic browsing audit category from M150, checking three metrics: accessibility tree, CLS visual stability, and WebMCP integration.

    Why it matters: The article details the specific checks in Lighthouse's new Agentic browsing audit and Chrome DevTools debugging methods, so developers can directly improve how agent-friendly their sites are.

Jun 9Tue
  1. Chrome Developers · AI and Browser65

    Join the WebMCP origin trial

    Chrome announced the WebMCP origin trial in Chrome 149, letting developers build structured tools for web apps that declare the purpose of buttons, input fields, and other UI elements and manage page state, so agents can complete tasks without guessing.

    Why it matters: With the official WebMCP origin trial open, developers can let agents operate web forms and debugging flows via declared tools and submit API feedback.

May 28Thu
  1. Mistral AI64

    Introducing Search Toolkit

    Mistral AI released the public preview of Search Toolkit, an open-source framework for building production-grade search pipelines for AI applications, unifying ingestion, retrieval and evaluation in a single shared-interface framework.

    Why it matters: The original details modules, evaluation metrics and enterprise case studies, helping readers judge whether it can replace their existing retrieval infrastructure.

May 27Tue
  1. Mistral AI68

    Build AI agents with the Mistral Agents API

    Mistral released a new Agents API that combines language models with built-in connectors, persistent memory across sessions, and agent orchestration, complementing the Chat Completion API.

    Why it matters: The official post details built-in connectors, stateful sessions and agent orchestration, plus SimpleQA comparison data, making it easier to evaluate enterprise-grade agent development.