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Агент в VS Code с актуальным поиском: как построить проверяемый контур для разработки

Агент в редакторе хорошо читает репозиторий, объясняет ошибки и предлагает патчи, но его ответ о быстро меняющемся API, недавнем релизе или редкой диагностике нельзя считать проверенным без внешних источников. Практичнее не заставлять модель угадывать, а дать ей управляемый инструмент поиска. Такой контур полезен, когда требуется быстро найти первоисточник, сравнить свежие изменения и сохранить ссылки, на которых основано решение. В этой статье разберём рабочую схему для VS Code: GitHub Copilot Chat в режиме Agent вызывает Serp MCP, получает результаты поиска, а затем использует их как входные данные для анализа кода и подготовки следующего шага. Управление ключом и приложениями находится в консоли приложений Ace Data Cloud ; обзор платформы доступен на русскоязычной странице платформы , а первичная документация — в разделе документов . Почему поиск должен быть инструментом, а не просто подсказкой в промпте У модели есть контекст диалога и знания, сформированные на момент обучения...
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How to Build an Image Generation and Editing Workflow with the Nano Banana Images API

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Most image features start simple: send a prompt, get a picture back. The complexity arrives when you need the same endpoint to support both first-time generation and edits based on one or more reference images. The Nano Banana Images API gives you that shape in a compact interface: one endpoint, two actions, and a response that is easy to track in a product workflow. What you can do The API described in the Ace Data Cloud documentation supports two core operations through POST /nano-banana/images : action: "generate" creates images from a text prompt . action: "edit" edits or combines existing images using image_urls plus a text prompt . count requests 1–4 images and defaults to 1. callback_url can be used when you prefer asynchronous completion through a webhook. This makes the API useful for builder-facing tasks such as product mockups, avatar variations, creative asset workflows, visual A/B test candidates, and reference-image edits wh...
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Getting Started with Nano Banana MCP for Image Generation and Editing in Your IDE

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If your image workflow already starts in an AI chat or coding editor, switching tabs to a separate image tool can break the flow. The Nano Banana MCP Server lets you keep that workflow inside clients such as Claude Desktop, VS Code, and Cursor by exposing image generation and editing as MCP tools. What you can do The Nano Banana MCP Server is documented as a bridge between MCP-compatible AI clients and Ace Data Cloud's Nano Banana image capabilities. Once configured, the client can call a small set of tools directly from the conversation or editor context. Generate images from text prompts with nanobanana_generate_image . Edit or combine existing images with nanobanana_edit_image . Try visual workflows such as virtual try-on and product placement, where an existing person, garment, or product image is part of the task. Track asynchronous work with nanobanana_get_task or batch status checks with nanobanana_get_tasks_batch . Choose among supported models :...
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Актуальный контекст в VS Code: подключаем веб-поиск к ИИ-агенту через MCP

Современный редактор с ИИ полезен ровно настолько, насколько он опирается на проверяемые данные. При работе с меняющимися библиотеками, облачными SDK, релизами фреймворков и редкими сбоями одной памяти модели недостаточно: сведения быстро устаревают, а уверенный ответ может оказаться неверным. Практичный способ сократить этот разрыв — подключить поиск как инструмент Model Context Protocol (MCP) и дать агенту в VS Code возможность запрашивать свежие источники в ходе задачи. Ниже — инженерный сценарий на основе документации Ace Data Cloud: установка расширения, хранение ключа в защищённом хранилище VS Code, проверка вызова, а также отдельные HTTP-примеры для диагностики интеграции. Подход ориентирован на разработчиков, работающих с несколькими моделями и желающих отделить поиск фактов от генерации кода. Что меняет MCP в редакторе MCP описывает единый способ, которым клиент с ИИ обнаруживает и вызывает внешние инструменты. В данном случае поисковый инструмент возвращает результаты ве...
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A Practical Guide to Running Claude Code in JetBrains IDEs

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When an AI coding assistant lives outside your editor, every useful change can turn into a copy-paste loop: ask in a terminal, inspect files in the IDE, apply diffs manually, then repeat. The Claude Code JetBrains plugin closes that gap by letting you run claude from the same project context while keeping JetBrains-native features such as diffs, selected code, open tabs, and diagnostics in the workflow. What you can do With the JetBrains integration configured, you can use Claude Code from IDEs such as IntelliJ IDEA, PyCharm, WebStorm, GoLand, PhpStorm, and Android Studio. The workflow is especially useful when you want an agentic coding session that can reason about the same files you are editing, then show code changes in the IDE instead of forcing you to reconstruct patches by hand. The integration supports several practical behaviors: Diff view: code changes are displayed in the IDE’s Diff viewer. Context selection: selected code and the current tab can be shared wit...
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How to Build a Reliable Image Editing Workflow with GPT-Image-2

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Image editing APIs are easy to demo, but harder to put into a production workflow: you need to keep the source structure stable, pass references cleanly, choose an output size intentionally, and avoid blocking your application while a render is running. What you can do The OpenAI Images Edits API on Ace Data Cloud exposes image editing through POST /openai/images/edits . The documented interface supports GPT Image models such as gpt-image-1 and gpt-image-2 , plus the nano-banana family through the same editing endpoint. For this guide, we will focus on gpt-image-2 , because the documentation describes it as stronger for structure preservation, text retention, direct URL input, base64 input, and high-resolution redrawing. In practical terms, that makes it useful for builder tasks like: turning an existing infographic into a dark-mode version while keeping its layout intact; replacing the style or environment of a product image while preserving key object placement; combining m...
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How to Build an Image Generation and Editing Workflow with the Nano Banana Images API

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When you wire image generation into a product, the challenge is not just producing one image. It is designing a workflow that handles prompt-only generation, reference-image editing, traceable results, and asynchronous completion without splitting your backend into unrelated code paths. What you can do The Nano Banana Images API exposes a single image endpoint for two practical workflows: text-to-image generation and image editing. The documented base URL is https://api.acedata.cloud , and the endpoint is POST /nano-banana/images . You choose the workflow with the action field. action: generate creates images from a text prompt . action: edit edits or combines existing images using image_urls and a prompt . count requests 1 to 4 images and defaults to 1. callback_url lets your service receive a POST JSON callback when work completes. How it works Requests are JSON. The required fields for generation are action and prompt . Editing also needs image_urls , an array with at l...
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