MCP server

City Atlas 城市圖譜 Official

tw.cityatlas/cityatlas

Checked 28 Oct 2026, 09:03 UTC · Connected to it and listed its tools

Greater Hsinchu, Taiwan open data on a 125 m grid: crashes, buses, schools, flood. Read-only.

Website

At a glance

Type

MCP server

Publisher

cityatlas.tw (verified by the MCP Registry)

Status

Working at last check

Tools

10

Token cost

About 2,287 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

28 Oct 2026, 09:03 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http cityatlas https://mcp.cityatlas.tw/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.cityatlas]
url = "https://mcp.cityatlas.tw/mcp"

Gemini CLI

{"mcpServers":{"cityatlas":{"httpUrl":"https://mcp.cityatlas.tw/mcp"}}}

Cursor

{"mcpServers":{"cityatlas":{"url":"https://mcp.cityatlas.tw/mcp"}}}

VS Code

{"servers":{"cityatlas":{"type":"http","url":"https://mcp.cityatlas.tw/mcp"}}}

Antigravity

agy mcp add cityatlas https://mcp.cityatlas.tw/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"cityatlas","server_url":"https://mcp.cityatlas.tw/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"cityatlas","server_url":"https://mcp.cityatlas.tw/mcp"}

Tools

• compare_change ~363 tokens
  兩個年份比,哪些里增加最多、減少最多(跟網站 07「看變化」同一套):里人口(97–114 年底,戶籍;97–106 只有新竹縣市、竹南、頭份,新設里設立前是估計、不拿來比)、事故場數(111–114)、里所得中位數(111→112)、投票率(總統 / 立委不分區政黨票 / 立委區域 / 縣市長,年份照選舉目錄)。門檻自己填(增加幾人 / 件 / 萬元 / 個百分點以上才算增加很多);里界調整過、兩屆不是同一塊地的里不比、列出原因。只描述差多少,不做因果。Which neighbourhoods grew or shrank most between two years (population
• compare_groups ~153 tokens
  有 B 的格 vs 沒有 B 的格,A 差多少 —— 在居民 × 道路長度切成的 25 層裡各比一次(避免被市中心拉著走)。只描述,不做因果。Compare cells with vs without B, within strata of residents and road length.
• describe_metric ~98 tokens
  一條資料的完整出處(機關、資料集、網址、取得日、檔案、對帳)、涵蓋哪些鄉鎮、統計、以及「可能在講別的事」的但書。Full provenance and caveats of one indicator.
• filter_cells ~268 tokens
  找出同時符合最多四個條件的 125 公尺格,回符合幾格、住了多少人,並照里 / 鄉鎮加總。例:離公車站超過 500 公尺、又住超過 100 人。Find grid cells that meet up to four conditions.
• lookup_place ~144 tokens
  給地址(例:竹北市光明六路 10 號)、里名(例:竹北市東平里)或鄉鎮名 → 那裡各項數字,以及在大新竹排第幾;查里名時另附里人口、所得、65 歲以上與 14 歲以下。地址只定位到 125 公尺的格(用我們自己的門牌檔比對,不送外部服務、不記錄)。Numbers for an address, neighbourhood or district.
• need_ranking ~236 tokens
  哪裡最多人被公車耽誤(被耽誤的人·分鐘、工作地、學校、公設的百分位加權);權重可帶,不帶用預設 60/20/10/10。Where the most people lose time on public transport compared with driving.
• overlay_metrics ~232 tokens
  兩條資料疊起來看:A(可換算)與 B 各切三等分成九宮格,回各格有幾個里 / 鄉鎮,並列出「兩樣都高」的。例:事故(每千居民)高、人行道少的里。Which places are high on two indicators at once.
• rank ~351 tokens
  某條資料在里或鄉鎮排前幾名(跟網站 07 同一套算法:先加總再除、人少的地方往平均拉)。可換算每千居民 / 每公里道路 / 路 + 人 + 店(只有事故)/ 任何能當分母的代號;可只看某個鄉鎮、可加篩條件。只回前 N 名。里層資料(長者占比、里所得、選舉投票率…)一個里一個數,不能換算、單位只能是里(人數可加到鄉鎮)。沒編里的範圍不進排名。Rank neighbourhoods or districts by an indicator, optionally normalised.
• search_catalog ~213 tokens
  用關鍵字找城市圖譜的資料:11 大類 82 項資料,細到 161 條指標(141 條格網:事故、公車、人行道、淹水、樹…;20 條里 / 鄉鎮 / 縣市:里人口、長者與兒童、里所得…),加上 47 屆選舉與公投(投票率、得票率);公開版少 Google 街景判讀的 2 項 4 條,實際查得到 80 項、157 條:回代號、名稱、單位、年份、出處機關、是不是推估、涵蓋幾區。Search the indicator catalog by keyword (82 datasets in 11 categories, 161 indicators: 141 grid + 20 neighbourhoo
• show_map ~229 tokens
  把一條資料(或「需要度」)畫成里 / 鄉鎮的五級地圖,在對話裡直接顯示,可拖曳縮放、滑過看數字;換算同「排名」。畫不出來的 AI 會拿到一個能點開的網址(請轉給使用者)。Draw an indicator as an interactive choropleth map of neighbourhoods; chats that cannot render it get a link that opens the map in a browser (pass it on to the user).

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