Conecta Claude, Cursor, Windsurf y otras herramientas de IA a más de 80 fuentes de datos ambientales y de infraestructura federales de EE. UU.
GeoTap agrega datos de FEMA, USGS, EPA, NOAA, USDA, USFWS, DOT, el Censo y más, accesibles a través del MCP (Protocolo de Contexto de Modelo).
Aplicación web: geotapdata.com — no requiere código, dibuja en un mapa y explora datos visualmente.
Node.js (v18 o posterior) — descárgalo aquí
Una dirección de correo electrónico para registrarse y obtener su clave API
Visite geotapdata.com/developers y regístrese con su correo electrónico para obtener una clave API gratuita.
Su clave API se enviará a su correo electrónico. Guárdela: la necesitará en el siguiente paso.
Elija su herramienta de IA a continuación y siga las instrucciones. El servidor MCP se instala automáticamente a través de npx; no es necesaria una descarga manual.
Abra Claude Desktop
Vaya a Settings (icono de engranaje) → Developer → Edit Config
Esto abrirá su archivo claude_desktop_config.json. Añada lo siguiente (reemplace your-api-key-here con su clave real):
{
"mcpServers": {
"geotap": {
"command": "npx",
"args": ["-y", "geotap-mcp-server"],
"env": {
"GEOTAP_API_KEY": "your-api-key-here"
}
}
}
}Ubicación del archivo de configuración:
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%APPDATA%\Claude\claude_desktop_config.json
Reinicie Claude Desktop por completo (cierre y vuelva a abrir)
Debería ver un icono de martillo (🔨) en la entrada de chat; eso significa que GeoTap está conectado
Ejecute este comando para añadir GeoTap a sus servidores MCP de Claude Code:
claude mcp add geotap -- npx -y geotap-mcp-serverLuego, establezca su clave API como una variable de entorno. Añada esto a su perfil de shell (~/.zshrc, ~/.bashrc, etc.):
export GEOTAP_API_KEY="your-api-key-here"Reinicie su terminal y luego inicie Claude Code. Las herramientas de GeoTap estarán disponibles automáticamente.
Abra Cursor
Vaya a Settings (⌘ + , en Mac, Ctrl + , en Windows) → busque "MCP"
Haga clic en "Edit in settings.json" o añada al archivo .cursor/mcp.json:
{
"mcpServers": {
"geotap": {
"command": "npx",
"args": ["-y", "geotap-mcp-server"],
"env": {
"GEOTAP_API_KEY": "your-api-key-here"
}
}
}
}Reinicie Cursor
Abra el panel de chat de IA: las herramientas de GeoTap aparecerán en la lista de herramientas disponibles
Abra Windsurf
Vaya a Settings → MCP Servers (o edite ~/.codeium/windsurf/mcp_config.json directamente)
Añada:
{
"mcpServers": {
"geotap": {
"command": "npx",
"args": ["-y", "geotap-mcp-server"],
"env": {
"GEOTAP_API_KEY": "your-api-key-here"
}
}
}
}Reinicie Windsurf
Para cualquier cliente MCP, el servidor se puede ejecutar directamente:
# Install globally
npm install -g geotap-mcp-server
# Run with your API key
GEOTAP_API_KEY=your-api-key-here geotap-mcpEl servidor se comunica a través de stdio: apunte su cliente MCP al comando geotap-mcp con la variable de entorno GEOTAP_API_KEY establecida.
Una vez conectado, pídale a su asistente de IA que obtenga datos para cualquier sitio en EE. UU. GeoTap recopila información de las más de 80 fuentes federales a la vez; solo proporcione una ubicación y luego pregunte lo que desee:
"Collect site data for 123 Main St, Austin TX"Una vez que los datos regresen (~60-120 segundos), puede hacer preguntas de seguimiento como:
"¿Está este sitio en una zona inundable?"
"¿Qué tipos de suelo hay aquí y cuál es el número de curva?"
"¿Hay preocupaciones de contaminación cerca?"
"¿Cuál es la precipitación de 100 años?"
"¿Qué permisos necesitaría para desarrollar este sitio?"
"Resuma los riesgos ambientales clave"
Problema | Solución |
El servidor no arranca / "GEOTAP_API_KEY is required" | Asegúrese de que su clave API esté configurada en el bloque |
"npx: command not found" | Instale Node.js (v18+), que incluye npx |
Las herramientas no aparecen en Claude Desktop | Reinicie Claude Desktop por completo (cierre + vuelva a abrir, no solo cierre la ventana) |
Errores de límite de tasa | Espere un momento y vuelva a intentarlo; los límites de ráfaga son por minuto |
Related MCP server: PropertyScoop MCP Server
Agencia | Datos disponibles |
FEMA | Zonas inundables, paneles FIRM, mapas de tasas de seguro contra inundaciones, límites de cauces de inundación |
USGS | Elevación (3DEP a 1m/10m/30m), geología, medidores de flujo, agua subterránea, uso de la tierra (NLCD), StreamStats, Estadísticas Nacionales de Flujo de Corrientes (NSS) |
EPA | Calidad del agua (ATTAINS), sitios Superfund, brownfields, liberaciones tóxicas TRI, USTs, salidas NPDES |
NOAA | Precipitaciones (Atlas 14), curvas IDF, estaciones de marea, proyecciones climáticas (CMIP6), estaciones meteorológicas, radar |
USDA/NRCS | Suelos (SSURGO), números de curva, grupos hidrológicos de suelo, parámetros TR-55 |
USFWS | Humedales (NWI), especies en peligro de extinción, hábitat crítico |
DOT | Puentes, túneles, Inventario Nacional de Puentes |
Censo | Datos demográficos, límites, datos geográficos TIGER |
USACE | Presas, diques, canales de navegación |
NHD | Líneas de flujo de corrientes, hidrografía, límites de cuencas hidrográficas (HUC-8/10/12) |
Otros | Plantas de energía, minas, tierras tribales, huellas de edificios y más |
Cada respuesta incluye la atribución de la fuente: la agencia federal, el nombre del conjunto de datos y la URL de referencia.
Variable | Descripción | Requerido |
| Su clave API del registro | Sí |
| Punto final de API personalizado (avanzado) | No |
Diseño de aguas pluviales: precipitaciones (Atlas 14, curvas IDF, hietogramas), números de curva, tiempo de concentración, descarga máxima
Análisis de inundaciones: frecuencia de inundaciones del Boletín 17C, curvas de duración de flujo, estimaciones de regresión regional
Delimitación de cuencas hidrográficas y entradas de modelos hidrológicos (HEC-HMS, SWMM)
Análisis de flujo bajo para permisos NPDES (7Q10, 7Q2, flujo medio armónico)
Tormentas de diseño ajustadas al clima para la resiliencia de la infraestructura
Debida diligencia ambiental para transacciones de propiedades
Puntuación de viabilidad y desarrollabilidad del sitio (escala 0-100)
Detección de zonas inundables, humedales y contaminación
Análisis de vías de permisos (Sección 404, NPDES, desarrollo en llanuras aluviales)
Recopilación de datos de escritorio para ESA Fase I (sitios de la EPA, calidad del agua)
Apoyo a la delimitación de humedales (NWI + suelos + hidrología)
Detección de hábitat de especies en peligro de extinción (hábitat crítico de USFWS)
Evaluación de deterioro de la calidad del agua (lista 303(d) de EPA ATTAINS)
¡Las contribuciones son bienvenidas! Por favor, abra un issue o un pull request.
MIT
Aplicación web: geotapdata.com
Problemas: 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.
