Подключите Claude, Cursor, Windsurf и другие ИИ-инструменты к более чем 80 федеральным источникам экологических и инфраструктурных данных США.
GeoTap агрегирует данные от FEMA, USGS, EPA, NOAA, USDA, USFWS, DOT, Census и других — доступ к ним осуществляется через протокол MCP (Model Context Protocol).
Веб-приложение: geotapdata.com — не требует программирования, рисуйте на карте и исследуйте данные визуально.
Node.js (v18 или новее) — скачать здесь
Адрес электронной почты для регистрации API-ключа
Перейдите на geotapdata.com/developers и зарегистрируйтесь с помощью своей электронной почты, чтобы получить бесплатный API-ключ.
Ваш API-ключ будет отправлен на вашу электронную почту. Сохраните его — он понадобится вам на следующем шаге.
Выберите ваш ИИ-инструмент ниже и следуйте инструкциям. MCP-сервер устанавливается автоматически через npx — ручная загрузка не требуется.
Откройте Claude Desktop
Перейдите в Settings (значок шестеренки) → Developer → Edit Config
Откроется ваш файл claude_desktop_config.json. Добавьте следующее (замените your-api-key-here на ваш реальный ключ):
{
"mcpServers": {
"geotap": {
"command": "npx",
"args": ["-y", "geotap-mcp-server"],
"env": {
"GEOTAP_API_KEY": "your-api-key-here"
}
}
}
}Расположение файла конфигурации:
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%APPDATA%\Claude\claude_desktop_config.json
Полностью перезапустите Claude Desktop (выйдите и откройте снова)
Вы должны увидеть значок молотка (🔨) в строке ввода чата — это означает, что GeoTap подключен
Выполните эту команду, чтобы добавить GeoTap в ваши MCP-серверы Claude Code:
claude mcp add geotap -- npx -y geotap-mcp-serverЗатем установите ваш API-ключ в качестве переменной окружения. Добавьте это в профиль вашей оболочки (~/.zshrc, ~/.bashrc и т.д.):
export GEOTAP_API_KEY="your-api-key-here"Перезапустите терминал, затем запустите Claude Code. Инструменты GeoTap будут доступны автоматически.
Откройте Cursor
Перейдите в Settings (⌘ + , на Mac, Ctrl + , на Windows) → выполните поиск "MCP"
Нажмите "Edit in settings.json" или добавьте в ваш файл .cursor/mcp.json:
{
"mcpServers": {
"geotap": {
"command": "npx",
"args": ["-y", "geotap-mcp-server"],
"env": {
"GEOTAP_API_KEY": "your-api-key-here"
}
}
}
}Перезапустите Cursor
Откройте панель ИИ-чата — инструменты GeoTap появятся в списке доступных инструментов
Откройте Windsurf
Перейдите в Settings → MCP Servers (или отредактируйте ~/.codeium/windsurf/mcp_config.json напрямую)
Добавьте:
{
"mcpServers": {
"geotap": {
"command": "npx",
"args": ["-y", "geotap-mcp-server"],
"env": {
"GEOTAP_API_KEY": "your-api-key-here"
}
}
}
}Перезапустите Windsurf
Для любого MCP-клиента сервер можно запустить напрямую:
# Install globally
npm install -g geotap-mcp-server
# Run with your API key
GEOTAP_API_KEY=your-api-key-here geotap-mcpСервер обменивается данными через stdio — укажите вашему MCP-клиенту команду geotap-mcp с установленной переменной окружения GEOTAP_API_KEY.
После подключения попросите вашего ИИ-ассистента получить данные для любого участка в США. GeoTap собирает данные из всех 80+ федеральных источников одновременно — просто укажите местоположение, а затем спрашивайте что угодно:
"Collect site data for 123 Main St, Austin TX"После получения данных (~60-120 секунд) вы можете задавать уточняющие вопросы, например:
"Находится ли этот участок в зоне затопления?"
"Какие здесь типы почв и каков коэффициент кривой (curve number)?"
"Есть ли поблизости опасения по поводу загрязнения?"
"Каков уровень осадков для 100-летнего периода?"
"Какие разрешения мне понадобятся для застройки этого участка?"
"Обобщите основные экологические риски"
Проблема | Решение |
Сервер не запускается / "GEOTAP_API_KEY is required" | Убедитесь, что ваш API-ключ установлен в блоке |
"npx: command not found" | Установите Node.js (v18+), который включает npx |
Инструменты не появляются в Claude Desktop | Полностью перезапустите Claude Desktop (выйдите + откройте снова, а не просто закройте окно) |
Ошибки ограничения скорости (Rate limit) | Подождите немного и повторите попытку — лимиты на всплески запросов действуют поминутно |
Related MCP server: PropertyScoop MCP Server
Агентство | Доступные данные |
FEMA | Зоны затопления, панели FIRM, карты страховых тарифов на случай наводнений, границы зон затопления |
USGS | Рельеф (3DEP с разрешением 1м/10м/30м), геология, гидропосты, грунтовые воды, землепользование (NLCD), StreamStats, Национальная статистика стока (NSS) |
EPA | Качество воды (ATTAINS), объекты Superfund, заброшенные промышленные зоны (brownfields), токсичные выбросы TRI, подземные резервуары (USTs), выпуски NPDES |
NOAA | Осадки (Atlas 14), кривые IDF, приливные станции, климатические прогнозы (CMIP6), метеостанции, радары |
USDA/NRCS | Почвы (SSURGO), коэффициенты кривой, гидрологические группы почв, параметры TR-55 |
USFWS | Водно-болотные угодья (NWI), исчезающие виды, критические места обитания |
DOT | Мосты, туннели, Национальный реестр мостов |
Census | Демография, границы, географические данные TIGER |
USACE | Дамбы, дамбы обвалования, судоходные каналы |
NHD | Линии водотоков, гидрография, границы водоразделов (HUC-8/10/12) |
Другое | Электростанции, шахты, земли коренных народов, контуры зданий и многое другое |
Каждый ответ включает ссылку на источник — федеральное агентство, название набора данных и справочный URL.
Переменная | Описание | Обязательно |
| Ваш API-ключ после регистрации | Да |
| Пользовательский API-эндпоинт (для продвинутых пользователей) | Нет |
Проектирование ливневой канализации: осадки (Atlas 14, кривые IDF, гиетограммы), коэффициенты кривой, время концентрации, пиковый расход
Анализ наводнений: частота наводнений по бюллетеню 17C, кривые продолжительности стока, региональные регрессионные оценки
Определение водоразделов и входные данные для гидрологического моделирования (HEC-HMS, SWMM)
Анализ низкого стока для разрешений NPDES (7Q10, 7Q2, гармоническое среднее значение стока)
Проектирование инфраструктуры с учетом климатических изменений
Экологическая комплексная проверка (due diligence) при сделках с недвижимостью
Оценка осуществимости и пригодности участка для застройки (шкала 0-100)
Проверка зон затопления, водно-болотных угодий и загрязнения
Анализ процесса получения разрешений (Раздел 404, NPDES, застройка пойм)
Сбор данных для экологической оценки Фазы I (объекты EPA, качество воды)
Поддержка при определении границ водно-болотных угодий (NWI + почвы + гидрология)
Проверка мест обитания исчезающих видов (критические места обитания USFWS)
Оценка нарушений качества воды (список EPA ATTAINS 303(d))
Приветствуем любой вклад! Пожалуйста, откройте issue или pull request.
MIT
Веб-приложение: geotapdata.com
Проблемы: GitHub Issues
npm: geotap-mcp-server
Collect comprehensive environmental data from ALL 80+ federal data sources for a site. Accepts an address, lat/lng coordinates, or a GeoJSON geometry (Point or Polygon). Returns a jobId — poll with get_results until complete (60-120 seconds).
Data returned covers: flood zones, wetlands, soils, geology, contamination sites, water quality, seismic risk, rainfall, infrastructure, ecology, energy, demographics, and much more.
| Name | Required | Description | Default |
|---|---|---|---|
| address | No | US street address (e.g., "123 Main St, Houston TX"). If provided, the site is geocoded automatically. Use this OR lat/lng OR geometry. | |
| lat | No | Latitude of the site (e.g., 34.8441). Use with lng. | |
| lng | No | Longitude of the site (e.g., -82.4010). Use with lat. | |
| geometry | No | GeoJSON geometry (Point or Polygon). For advanced use — most users should use address or lat/lng instead. | |
| bufferAcres | No | Site area in acres when using a point location. Creates a circular buffer. Default: 1 acre. Range: 0.1–640. | |
| searchRadiusMiles | No | How far to search for nearby features (contamination, infrastructure, etc.). Default: 3 miles. Range: 0.5–10. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses async behavior, polling requirement, time estimate, and broad data coverage. It does not mention rate limits or availability constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two front-loaded paragraphs. Every sentence adds value with no redundancy or waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description covers the return format (jobId), polling mechanism, and data categories. It is fairly complete but could mention error handling or limits.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for each parameter. The description adds value by summarizing the OR condition among address/lat-lng/geometry and providing default ranges for bufferAcres and searchRadiusMiles.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool collects comprehensive environmental data from 80+ federal sources, accepting address, lat/lng, or geometry. It uses a specific verb-resource combo and distinguishes from siblings like get_results.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains how to use the tool (provide location input, receive jobId, poll with get_results) and gives typical completion time. It does not explicitly state when not to use it, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the GeoTap API discovery document. Returns a structured description of all data sources and capabilities.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It states the return value but omits side effects, authentication needs, rate limits, or other behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler. Every sentence adds value: first states the verb and resource, second describes the return content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema, the description should fully describe the return value. It says 'structured description' but lacks detail on format or structure, which is a gap for a tool that only outputs data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With zero parameters, schema coverage is 100% automatically. The description adds no parameter details, but baseline for 0 params is 4, which is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it gets the GeoTap API discovery document, specifying the resource and action. It distinguishes from siblings 'collect_site_data' and 'get_results', which imply different operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. It does not mention that it should be used to discover capabilities before performing other actions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check the status of a data collection job and retrieve results. Poll every 10 seconds until status is "completed".
When complete, present results as a SCANNABLE ENGINEERING DOCUMENT — not a data dump. An engineer needs to answer: (1) What kills the project? (2) What complicates permitting? (3) What's the baseline context?
CRITICAL FLAGS — scan results FIRST and lead with these if present:
FEMA Zone AE/AO/VE or SFHA=true → "Site intersects SFHA — Zone [X]" (CRITICAL)
Floodway present → "Regulatory floodway — no-rise certification required" (CRITICAL)
Superfund count > 0 → "NPL Superfund site within search radius" (CRITICAL)
Wetland count > 10 → "High wetland density — Section 404 permitting likely" (HIGH)
303(d) impaired water → "TMDL required, stricter discharge limits" (HIGH)
Brownfields > 3 → "Phase I ESA recommended" (MODERATE)
Soils with HSG D → "Poorly draining soils — stormwater design impact" (MODERATE)
SECTION ORDER (skip sections with no data):
Site overview — address, coordinates, county, elevation range, land cover, area
FEMA flood zones — table: zone | subtype | SFHA | risk level
Soils — per unit: HSG, drainage class, slope, flood frequency, building/septic limitations
Atlas 14 rainfall — IDF table (rows: 15min, 1hr, 6hr, 12hr, 24hr, 3day; cols: 2yr–100yr). Bold the 24hr row. Include Atlas 14 volume.
Natural hazard risk — NRI ratings by hazard type
Wetlands — count, type breakdown, Section 404 note
Water resources — streams with distances, impaired waters
Contamination — Superfund, brownfields (with distances/status), USTs, NPDES
Seismic & dams — ASCE 7-22 params (SDS, SD1, SDC), nearby dams with hazard rating
Infrastructure — hospitals, fire stations, schools, EMS counts
Demographics — population, median income, vacancy rate
FORMATTING:
Use markdown tables for flood zones, rainfall IDF, soils, brownfields
Cite source agencies (FEMA, NRCS, NOAA Atlas 14, EPA) not just "GeoTap"
Include distances and bearings for nearby features (e.g., "0.8 mi NW")
"_noData: true" means queried but nothing found — mention where relevant ("no Superfund sites" is positive)
End with: "Data sourced from US federal agencies via GeoTap. Verify critical findings before engineering or regulatory decisions."
| Name | Required | Description | Default |
|---|---|---|---|
| jobId | Yes | Job ID returned from collect_site_data |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations present, so description must disclose behavior. It explains polling mechanism and that it returns results, but does not mention failure modes, timeouts, or potential side effects. Still, it provides more than minimal info.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is extremely long (500+) with extensive output formatting instructions. While front-loaded with purpose, the verbosity reduces conciseness. Could be more succinct.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, so description fully specifies output handling: critical flags, section order, formatting, and source attribution. Complete guidance for the agent to present results correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only one parameter with 100% schema coverage. Schema description is adequate. Description does not add new meaning to the parameter beyond the schema, maintaining baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool checks status and retrieves results from a data collection job, with specific polling advice. It distinguishes from sibling tools like collect_site_data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives explicit polling guidance (every 10 seconds until completed) and implies use after collect_site_data. Lacks explicit when-not-to-use or alternatives, but context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v3.0.5collect_site_dataget_llms_txtget_resultsScored across 3 tools
Each tool has a distinct purpose: collect_site_data starts data collection, get_results polls job status, and get_llms_txt provides API documentation. No overlap in functionality.
Tool names follow a consistent verb_noun pattern (e.g., collect_site_data, get_results), but get_llms_txt uses an abbreviation (llms) that is slightly less conventional, though still clear.
With only three tools, the server is minimal but appropriate for its focused scope: initiating data collection, polling for results, and accessing API documentation. It could benefit from a cancellation tool, but the count is reasonable.
The tool set covers the core workflow: submit job (collect_site_data), retrieve results (get_results), and discover capabilities (get_llms_txt). Missing options like canceling a job or listing previous jobs are minor omissions for a single-purpose API.
Weather, climate, terrain, fire and flood risk data for any point or polygon on Earth. Free tier.
Federal environmental records near any US location, with dates and provenance; never a safety score.
US site diligence for agents: parcels, power, flood, zoning rules and more, each with its source.
FEMA flood zone, hazard history and demographics for any US address, with sources on every answer.
