Which integrations are available for this server?

Uses Gemini Flash as the internal LLM agent to orchestrate travel itinerary creation, refinement, and confirmation through parallel fan-out to downstream MCP services. Implements a LangGraph agent with StateGraph and parallel fan-out capabilities to coordinate multiple downstream MCP servers for comprehensive travel itinerary management. Deployed on Railway with RAILPACK builder, providing HTTP/SSE endpoints for MCP communication, OAuth 2.1 authentication, and health monitoring.

How do I use Agentic MCP Itinerary?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Agentic MCP Itinerary Plan a 4-day trip to Tokyo with flights and hotels, budget $2500." That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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Agentic MCP Itinerary

by AlexGHerrera

Overview Schema Related Servers Score Discussions

Python

Hybrid

Agentic MCP Itinerary — PoC

Un MCP server que internamente corre un agente LLM (Gemini Flash + LangGraph) y orquesta múltiples MCP servers downstream. El cliente (Claude Desktop, ChatGPT) ve una interfaz limpia con estado persistente entre iteraciones.

Concepto

Claude Desktop / ChatGPT
        │
        │  MCP (HTTP/SSE + OAuth 2.1)
        ▼
┌─────────────────────────────────────┐
│         travel-agent (este repo)    │
│  FastMCP server + LangGraph agent   │
│                                     │
│  ┌──────┐  ┌────────┐  ┌──────────┐│
│  │Vuelos│  │Hoteles │  │Actividad.││  ← MCP mocks STDIO
│  └──────┘  └────────┘  └──────────┘│
└─────────────────────────────────────┘

¿Por qué esto es difer? Ninguna empresa ofrece todavía un "agente vertical empaquetado como MCP server". Este PoC demuestra el patrón: el cliente solo ve 4-5 tools limpias, pero detrás hay un agente con memoria, fan-out paralelo y estado persistente.

Stack

Componente	Tecnología
Servidor MCP expuesto	FastMCP 3.1.1 (`streamable-http`)
Agente interno	LangGraph (`StateGraph` + fan-out paralelo)
Modelo LLM	Gemini Flash (`gemini-2.0-flash`)
Auth	OAuth 2.1 Authorization Code Flow + JWT HS256
Checkpointing	`MemorySaver` (en memoria, suficiente para PoC)
MCP downstream	MCP SDK oficial (`mcp.client.stdio`)
Mocks	3 FastMCP servers STDIO (vuelos, hoteles, actividades)
Deploy	Railway (RAILPACK + pyproject.toml)

Tools expuestas (API pública)

Tool	Parámetros	Descripción
`create_itinerary`	`requirements: str`	Crea un draft completo (vuelos + hotel + actividades en paralelo)
`refine_itinerary`	`itinerary_id: str`, `change_request: str`	Refina un borrador existente
`get_itinerary`	`itinerary_id: str`	Recupera el estado actual
`list_itineraries`	—	Lista todos los itinerarios activos
`confirm_itinerary`	`itinerary_id: str`	Confirma y genera `confirmation_code`

Deploy en Railway

URLs

Health: https://travel-agent-production-c1c4.up.railway.app/health
MCP endpoint: https://travel-agent-production-c1c4.up.railway.app/mcp
OAuth metadata: https://travel-agent-production-c1c4.up.railway.app/.well-known/oauth-authorization-server
Login form: https://travel-agent-production-c1c4.up.railway.app/oauth/authorize

IDs Railway

Proyecto: e50da57f-ee0b-47a3-81a3-55556fe6de0d
Servicio: 09065312-ac84-4876-b9c9-dd5d6439f1d4
Environment: 09b3f0c9-e5ad-4f61-b351-275bbcffd5ad

Variables de entorno requeridas

Variable	Descripción
`GEMINI_API_KEY`	API key de Google Gemini
`MCP_USERNAME`	Usuario para el login OAuth
`MCP_PASSWORD`	Contraseña para el login OAuth
`MCP_JWT_SECRET`	Secreto para firmar JWT (generado con `secrets.token_urlsafe(32)`)
`MCP_BASE_URL`	URL pública del servidor (para construir redirect URIs)

Auth: OAuth 2.1 Authorization Code Flow

Flujo completo

1. Claude Desktop detecta el MCP server
2. Descubre /.well-known/oauth-authorization-server
3. Redirige al usuario a /authorize
4. El servidor redirige a /oauth/authorize (form de login HTML)
5. Usuario introduce user/pass → POST /oauth/authorize
6. Servidor valida credenciales (MCP_USERNAME / MCP_PASSWORD)
7. Emite auth code → redirect a Claude Desktop
8. Claude Desktop intercambia code → JWT en /token
9. JWT usado como Bearer en todas las llamadas MCP

Implementación

server/auth.py: SimpleOAuthProvider (extiende OAuthProvider de FastMCP)
JWT HS256, 1h de validez
Auth codes: 5 min de validez
PKCE (S256) soportado
/health permanece público sin auth

Configurar Claude Desktop

Edita ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "travel-agent": {
      "type": "http",
      "url": "https://travel-agent-production-c1c4.up.railway.app/mcp"
    }
  }
}

Sin headers — Claude Desktop gestiona el OAuth flow automáticamente. La primera vez abrirá el browser para el login.

Desarrollo local

Requisitos

pip install -e ".[dev]"

Arrancar servidor

PYTHONPATH=server MCP_USERNAME=alexguerra MCP_PASSWORD=tu_pass \
  MCP_JWT_SECRET=dev_secret python3 server/main.py

Smoke test

PYTHONPATH=server python3 tests/smoke_test.py

Verificar sintaxis

PYTHONPATH=server python3 -m py_compile server/main.py server/auth.py server/agent.py

Estructura del proyecto

agentic-mcp-itinerary/
├── server/
│   ├── main.py          # FastMCP server (4 tools + OAuth + /health)
│   ├── auth.py          # SimpleOAuthProvider (OAuth 2.1 + JWT)
│   ├── agent.py         # LangGraph graph con fan-out paralelo
│   ├── state.py         # ItineraryState TypedDict + checkpointer
│   └── tools/
│       ├── flights.py   # Cliente MCP → mock vuelos
│       ├── hotels.py    # Cliente MCP → mock hoteles
│       └── activities.py # Cliente MCP → mock actividades
├── mocks/
│   ├── flights_mcp.py   # Mock server vuelos (FastMCP STDIO)
│   ├── hotels_mcp.py    # Mock server hoteles (FastMCP STDIO)
│   └── activities_mcp.py # Mock server actividades (FastMCP STDIO)
├── tests/
│   └── smoke_test.py    # Test end-to-end básico
├── docs/
│   └── OAUTH_PLAN.md    # Spec del OAuth (referencia de diseño)
├── pyproject.toml       # Deps para RAILPACK
├── railway.toml         # Builder=RAILPACK, startCommand
└── claude_desktop_config.json  # Config para Claude Desktop (sin Bearer manual)

Historial de decisiones clave

Decisión	Alternativa descartada	Motivo
RAILPACK + pyproject.toml	nixpacks	nixpacks falla en pip dentro de env inmutable
OAuth 2.1 Authorization Code	Static Bearer token	Claude Desktop gestiona OAuth nativo; más producción-ready
JWT HS256 en memoria	DB de tokens	PoC — sin estado persistente entre reinicios
FastMCP 3.1.1 `OAuthProvider`	Auth manual con Starlette	FastMCP integra el flow con el transport MCP
`MemorySaver`	SQLite/Redis	Suficiente para PoC local; fácil migrar a SqliteSaver
Gemini Flash	Claude Haiku	Codex tenía conflicto de credenciales con Anthropic

Próximos pasos (post-PoC)

Test en Claude Desktop — verificar OAuth flow completo
Persistencia real — SqliteSaver o Postgres para estado entre reinicios
Downstream MCP reales — reemplazar mocks por APIs reales (Amadeus, Booking, etc.)
Multi-usuario — DB de users en lugar de env vars
Rate limiting — por token JWT
Telemetría — LangSmith o similar para trazar el agente interno

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