Aviation Model Context Protocol
MCP de aviación: Servidores de protocolo de contexto de modelo para datos de aviación
Aviation MCP ofrece un conjunto de servidores de Protocolo de Contexto de Modelo (MCP) que se asignan a las API de la FAA y otras plataformas de aviación, lo que facilita la integración de datos de aviación en tiempo real en sus flujos de trabajo basados en LLM. Este proyecto está diseñado para desarrolladores que desean conectar sus clientes LLM (como Cursor, Claude u otros) con fuentes de datos de aviación fiables para información meteorológica, NOTAM, cartas náuticas, información de aeronaves y más.
⚠️ Descargo de responsabilidad ⚠️
El desarrollador de este código no se responsabiliza de la exactitud ni la seguridad de las API que proporcionan datos, ni de la planificación de su vuelo en particular. Esto aplica tanto al software como a las instrucciones de FlightPlanning.md , que NO sustituyen la experiencia de un piloto con la licencia correspondiente . El piloto al mando es el único responsable de la seguridad del vuelo y del cumplimiento de la normativa aplicable.
Características
Servidores MCP modulares para datos de aviación
Se integra con la FAA, Aviation Weather y otras API.
Fácil configuración para usar con cualquier cliente LLM compatible con MCP
Publicado como un paquete npm:
aviation-mcp
Related MCP server: Aviation MCP Server
Uso del servidor MCP
Agregue el servidor aviation-mcp a su archivo mcp.json como se indica a continuación. Asegúrese de actualizar las claves para que contengan valores válidos (visite https://api.faa.gov/s/ para obtener las credenciales de cliente de la API de la FAA, https://api-ninjas.com/ para obtener sus claves de API) o elimínelas (las API correspondientes se ocultarán).
El clima de aviación (incluidos numerosos datos georreferenciados) y las cartas no requieren claves API. Los NOTAM requieren un ID/secreto de cliente de la FAA.
{
"mcpServers": {
"aviation": {
"command": "npx",
"args": [
"-y",
"aviation-mcp"
],
"env": {
"API_NINJA_KEY": "<your-key>",
"FAA_CLIENT_ID": "<your-id>",
"FAA_CLIENT_SECRET": "<your-secret>"
}
}
}
}Fuentes oficiales
tiempo : Datos meteorológicos de aviación (METAR, TAF, PIREP, SIGMET, G-AIRMET, etc.)
Cartas : seccionales, TAC, IFR en ruta y cartas TPP
notam : API de NOTAM de la FAA
🚧 Fuentes rotas 🚧
Estas fuentes serían útiles, pero la integración o el acceso a la API aún no funcionan:
Precipitación : API de proximidad meteorológica EIM de la FAA (datos de precipitación)
Aeropuertos : información de la FAA sobre aeropuertos y pistas
No implementado
retrasos : la API ASWS de la FAA proporciona información sobre retrasos en el aeropuerto.
🚧🚧Fuentes no oficiales🚧🚧
aeronave : Datos de la aeronave
🚧 Fuentes faltantes 🚧
Rutas de procedimiento en formato legible por máquina. TODO: Descargar datos CIFP y usar un método como arinc424 para transformarlos a un formato utilizable.
Datos del espacio aéreo en formato legible por máquina. TAREAS: Descargar datos de NASR y usar una biblioteca para leer archivos shape o datos AIXM.
Uso
Una vez configurado, su cliente LLM puede conectarse a los servidores MCP y consultar datos de aviación según sea necesario. Consulte la documentación de su cliente para obtener más información sobre cómo proporcionar la configuración mcp.json .
Consulte FlightPlanning.md para obtener un ejemplo de mensaje del sistema que se puede utilizar para la planificación del vuelo.
Para la conciencia temporal, recomiendo combinar con el tiempo .
Para la gestión de EFB, considere combinarlo con el sistema de archivos o gdrive .
Cobertura de API
Para obtener una lista detallada de las API, los puntos finales y el estado de integración compatibles, consulte Sources.md .
Licencia
Instituto Tecnológico de Massachusetts (MIT)
Available Tools
1 toolget_notamsC
Retrieves NOTAMs based on specified filters
| Name | Required | Description | Default |
|---|---|---|---|
| classification | No | The NOTAM classification | |
| domesticLocation | No | The domestic location criteria (e.g., 'IAD' for Dulles International Airport) | |
| effectiveEndDate | No | The effective end date | |
| effectiveStartDate | No | The effective start date | |
| featureType | No | The feature type filter | |
| icaoLocation | No | The ICAO location criteria (e.g., 'KIAD' for Dulles International Airport) | |
| lastUpdatedDate | No | The last update date | |
| locationLatitude | No | The location latitude (e.g., 60.57) | |
| locationLongitude | No | The location longitude (e.g., -151.24) | |
| locationRadius | No | The location radius in nautical miles (max: 100) | |
| notamNumber | No | The NOTAM number (e.g., 'CK0000/01') | |
| notamType | No | The NOTAM type: 'N' for New, 'R' for Replaced, 'C' for Canceled | |
| pageNum | No | The page number | |
| pageSize | No | The page size (max: 1000) | |
| responseFormat | No | Response format for NOTAM data | geoJson |
| sortBy | No | The field to sort results by | |
| sortOrder | No | The sort order |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states retrieval with filters. It lacks behavioral details like pagination handling (implied by pageNum/pageSize but not explained), rate limits, authentication needs, or response format implications (e.g., geoJson output). This leaves significant gaps in understanding tool behavior.
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 a single, efficient sentence that front-loads the core action and scope without unnecessary words. It's appropriately sized for its purpose, with zero 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?
For a complex tool with 17 parameters, no annotations, and no output schema, the description is inadequate. It doesn't address behavioral aspects like pagination, response formats, or error handling, leaving the agent with insufficient context to use the tool effectively.
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 description coverage is 100%, so parameters are well-documented in the schema. The description adds no extra meaning beyond implying filters exist, which the schema already details. Baseline 3 is appropriate as the schema handles parameter semantics effectively.
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 verb ('Retrieves') and resource ('NOTAMs'), with scope ('based on specified filters'). It's specific about the action and subject matter, though without sibling tools to differentiate from, it can't achieve the highest score for sibling differentiation.
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 is provided on when to use this tool, such as scenarios or prerequisites. The description mentions filters but doesn't explain their application or alternatives, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v1.0.0- First observed
get_notams
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'get_notams' has a clear, distinct purpose focused on retrieving NOTAMs.
The tool name 'get_notams' follows a clear verb_noun pattern (get + notams). Since there is only one tool, consistency is inherently perfect with no deviations or mixed conventions.
The server has only one tool, which feels thin for a domain like aviation that typically involves multiple operations such as querying weather, flight plans, or airspace data. This minimal toolset limits functionality and suggests an incomplete surface.
The toolset is severely incomplete for an aviation context. It only provides NOTAM retrieval, with no coverage for other essential aviation data like METARs, TAFs, flight tracking, or airspace information, leading to significant gaps in agent workflows.
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