了解 CARTO 如何将地理空间分析能力集成至 Claude、ChatGPT 及任何兼容 MCP 的人工智能系统——涵盖工作流工具、交互式地图以及专为地理空间任务构建的命令行界面（CLI）。

Discover how CARTO brings geospatial analysis to Claude, ChatGPT, and any MCP-compatible AI — from workflow tools to interactive maps to a purpose-built CLI.

我们构建和使用软件的方式正在发生根本性转变——这种转变并非发生在边缘，而是深入核心。智能体（agentic）AI 平台的兴起，正在重塑团队与数据及分析工具的交互方式；地理信息系统（GIS）也不例外。我们认为，这是数十年来 GIS 领域最具意义的一次平台级变革。我们希望 CARTO 成为组织开展地理空间分析的最佳平台，并支持其从任意所选界面规模化部署 AI 智能体。本文概述了 CARTO 为实现这一愿景所采取的多种路径：将地理空间工作流封装为可供任意 AI 智能体调用的工具；将交互式地图可视化直接嵌入对话中；并构建一套命令行接口（CLI）与一系列“技能”（Skills），使智能体能够代表用户全面自动化地理空间任务。 去年十月，我们推出了 CARTO MCP Server——这是业内首批地理空间 MCP 实现之一，也是我们“智能体化 GIS”（Agentic GIS）战略的起点。在实际应用中，这体现为向 Claude 提问，要求其对一组地点执行邻近性分析；或由 ChatGPT 智能体在更大型自动化流水线中触发风险评估工作流。分析过程在数据仓库中完成，CARTO 提供地理空间分析能力，而智能体负责编排调度。 仅靠地理空间分析生成的文本输出，作用终究有限。得知你旗下 47 家门店位于洪涝风险区内，固然具有信息价值；但若能在地图上直观看到这些门店沿某段海岸线聚集分布、按风险等级着色分层呈现，则是一种本质不同的认知方式。 注：CARTO MCP App 当前仍为实验性原型，随着开发推进，其形态可能发生显著变化。若您有意体验该原型，或希望了解后续路线图规划，我们非常期待您的反馈。 地理空间分析极少仅依赖单条查询即可完成，它通常涉及多步骤工作流：数据准备、分析执行、迭代优化、结果审阅与成果发布。为支持智能体应对此类复杂度，我们所需的不止是零散的工具调用能力——更需要能端到端理解如何使用 CARTO 的智能体。为此，我们正将 CLI 与 Skills 相结合：后者是一组经精心设计的指令集，不仅告诉 AI 智能体 CARTO 能做什么，更指导其如何运用 CARTO 开展地理空间分析。可将其视作一种按需加载的领域专业知识——相当于以培训新员工熟悉空间数据技术栈的方式，为 AI 智能体完成“入职”。 我们所致力于构建的，是一个无论技术背景如何，人人都能开展地理空间分析的世界。这并非因为复杂性已被消除，而是因为 AI 智能体已代为驾驭其中的复杂性。 更长远来看，是智能体之间的协同编排：一个企业级通用 AI 智能体（代表业务团队运行的通用助手）调用一系列专业化的地理空间智能体，分别承担网络分析、风险建模、区域划分优化等不同任务。通用智能体负责委派，专业智能体负责执行，最终用户获得答案。 我们才刚刚起步，但方向已然清晰，这场变革势不可挡。我们迫不及待要向您展示接下来的一切。 目前，CARTO Workflows 已提供逾 200 种空间组件，使智能体可执行从基础地理编码与缓冲区分析，到复杂的多步骤选址或服务范围建模等各类分析任务；所有组件均作为 MCP 工具开放，供智能体随时调用。 Javier 是 CARTO 首席产品官（CPO），在全球范围内负责 CARTO 的产品组合、客户体验及创新路线图。其职责覆盖完整的产品开发与客户体验周期。他是一名兼具软件工程与经济学背景的产品经理，致力于将技术应用于常被忽视的重要议题，并打造切实改善人们生活的新产品。 将 CARTO MCP Server 接入 Microsoft Copilot Studio，即可让每个团队在 Microsoft 365 环境中，以自然语言获取地图、应用与空间分析能力。

Something fundamental is shifting in the way we build and use software. Not at the margins, but at the core. The rise of agentic AI platforms is changing how teams interact with data and analytical tools, and GIS is not going to be any different. We believe this is one of the most significant platform shifts in GIS in decades. And we want CARTO to be the best place for geospatial analysis, for organizations to scale AI agents from whatever interface they choose. This post gives an overview of the different ways CARTO is working to make that real: exposing geospatial workflows as tools that any AI agent can invoke, bringing interactive map visualizations directly inside conversations, and building a CLI and a set of Skills that allow agents to fully automate geospatial work on behalf of the user. Last October, we launched the CARTO MCP Server, one of the first geospatial MCP implementations, and the starting point of our Agentic GIS strategy. In practice, this looks like asking Claude to run a proximity analysis against a set of locations, or having a ChatGPT agent trigger a risk assessment workflow as part of a larger automated pipeline. The analysis happens in the data warehouse, CARTO provides the geospatial analysis capabilities, and the agent orchestrates it. Text output from geospatial analysis will only take you so far. Knowing that 47 of your stores fall within a flood risk zone is informative. Seeing them on a map, clustered along a particular stretch of coastline, concentrated in zones shaded by risk level, is a fundamentally different type of understanding. Note: the CARTO MCP App is currently an experimental prototype and may change significantly as we develop it further. If you're interested in exploring it or learning what's on the roadmap, we'd love to hear from you. Geospatial analysis is rarely a single query. It involves multi-step workflows: preparing data, running analysis, iterating, reviewing, publishing. To support agents operating at that level of complexity, we need more than individual tool calls — we need agents that understand how to work with CARTO end-to-end. We're pairing the CLI with Skills: curated instruction sets that teach AI Agents not just what CARTO can do, but how to do geospatial analysis with it. Think of them as domain expertise that agents can load on demand, the equivalent of onboarding an AI agent the way you'd onboard a new team member to your spatial data stack. What we're building toward is a world where anyone can do geospatial analysis, regardless of technical background. Not because the complexity has been removed, but because AI Agents navigate it for them. Further out, agent-to-agent orchestration: an enterprise AI agent (a general-purpose assistant acting on behalf of a business team) calling a suite of specialized geospatial agents for different tasks: one for network analysis, one for risk modeling, one for territory optimization. The general agent delegates; the specialists execute; the user gets answers. We're just getting started. But the direction is clear, and the transformation is unstoppable. We can't wait to show you what's next. Over 200 spatial components are available through CARTO Workflows, letting agents run analyses ranging from simple geocoding and buffering to complex multi-step site selection or catchment area modeling, all exposed as MCP tools an agent can call. Javier is CARTO's Chief Product Officer and is globally responsible for CARTO’s product portfolio, customer experience, and innovation roadmap. Javier's responsibilities span the complete product development and customer experience cycle. Product Manager with a background in software engineering and economics. Driven to apply tech to overlooked issues and create new products that improve people's lives. Connect the CARTO MCP Server to Microsoft Copilot Studio and give every team maps, apps, and spatial analysis in plain language, inside Microsoft 365.