mcp-agent-langchainjs MCP — Casos de uso, Instalar & Demo en vivo

Por qué usarlo

Características clave

LangChain.js agent with MCP tool integration (reference pattern)
Serverless deploy — Static Web Apps + Azure Functions + Cosmos DB
IaC via Bicep templates
User auth + session history
Local dev via Ollama or Codespaces; production via azd up

Demo en vivo

Cómo se ve en la práctica

agent-langchainjs.replay ▶ listo

0/0

Instalar

Elige tu cliente

~/Library/Application Support/Claude/claude_desktop_config.json · Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "agent-langchainjs": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-agent-langchainjs"
      ],
      "_inferred": true
    }
  }
}

Abre Claude Desktop → Settings → Developer → Edit Config. Reinicia después de guardar.

~/.cursor/mcp.json · .cursor/mcp.json

{
  "mcpServers": {
    "agent-langchainjs": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-agent-langchainjs"
      ],
      "_inferred": true
    }
  }
}

Cursor usa el mismo esquema mcpServers que Claude Desktop. La configuración del proyecto prevalece sobre la global.

VS Code → Cline → MCP Servers → Edit

{
  "mcpServers": {
    "agent-langchainjs": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-agent-langchainjs"
      ],
      "_inferred": true
    }
  }
}

Haz clic en el icono MCP Servers de la barra lateral de Cline y luego en "Edit Configuration".

~/.codeium/windsurf/mcp_config.json

{
  "mcpServers": {
    "agent-langchainjs": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-agent-langchainjs"
      ],
      "_inferred": true
    }
  }
}

Mismo formato que Claude Desktop. Reinicia Windsurf para aplicar.

~/.continue/config.json

{
  "mcpServers": [
    {
      "name": "agent-langchainjs",
      "command": "npx",
      "args": [
        "-y",
        "mcp-agent-langchainjs"
      ]
    }
  ]
}

Continue usa un array de objetos de servidor en lugar de un mapa.

~/.config/zed/settings.json

{
  "context_servers": {
    "agent-langchainjs": {
      "command": {
        "path": "npx",
        "args": [
          "-y",
          "mcp-agent-langchainjs"
        ]
      }
    }
  }
}

Añádelo a context_servers. Zed recarga en caliente al guardar.

claude mcp add agent-langchainjs -- npx -y mcp-agent-langchainjs

Un solo comando. Verifica con claude mcp list. Quita con claude mcp remove.

Casos de uso

Usos del mundo real: mcp-agent-langchainjs

Bootstrap a serverless agent on Azure with MCP tool calls

👤 Azure devs building AI features ⏱ ~120 min advanced

Cuándo usarlo: You want to ship an LLM-powered feature on Azure and need a working reference to fork.

Requisitos previos

Azure subscription — azure.microsoft.com — free tier covers dev
Azure Developer CLI — brew install azd or Windows installer

Flujo

Fork and deploy

Fork Azure-Samples/mcp-agent-langchainjs and walk me through azd up to deploy to my Azure sub.✓ Copiado

→ Live Azure URL + Functions + Cosmos provisioned
Swap the demo tool

Replace the burger-ordering MCP with a custom MCP for my domain (e.g. appointment booking). Show me the wiring.✓ Copiado

→ Code diff + working custom tool
Customize the UI

The sample has a chat UI; customize brand/colors and the welcome message.✓ Copiado

→ Styled app

Resultado: A shippable Azure-hosted agent derived from a vetted sample.

Errores comunes

Free-tier Azure OpenAI has low quota — Provision your own OpenAI resource in a region with capacity; set endpoint in env
Local Ollama doesn't handle complex tool calls well — Use a cloud model (GPT-4o-mini, etc.) for dev that involves multi-step tool calls

Learn the MCP + LangChain.js integration pattern

👤 Devs new to MCP ⏱ ~60 min intermediate

Cuándo usarlo: You're evaluating MCP and want to see how it plugs into the LangChain.js ecosystem.

Flujo

Read the code

Summarize how this repo wires MCP to LangChain.js agents. What's the key integration point?✓ Copiado

→ Architecture explanation
Run locally

Run it in Codespaces. Exercise the burger-order flow. Observe the MCP tool calls in logs.✓ Copiado

→ Working local run + tool call traces

Resultado: Hands-on understanding of the pattern before building your own.

Combinaciones

Combínalo con otros MCPs para multiplicar por 10

agent-langchainjs + github

CI/CD the sample to your own fork

Fork the repo, set up GitHub Actions to azd deploy on push to main.✓ Copiado

Herramientas

Lo que expone este MCP

Herramienta	Entradas	Cuándo llamar	Coste
(reference app — not a callable MCP)	N/A	This is a sample app you deploy, not a tool Claude calls	N/A

Coste y límites

Lo que cuesta ejecutarlo

Cuota de API: Azure consumption-based
Tokens por llamada: N/A — you're building the app, not calling it as a tool
Monetario: Varies — cheap on free tier for dev; production costs depend on traffic
Consejo: Use Azure cost alerts early. Cosmos DB can be expensive if mis-provisioned — keep it on serverless tier during dev.

Seguridad

Permisos, secretos, alcance

Almacenamiento de credenciales: Azure Key Vault + Managed Identity (set up by the Bicep templates)

Salida de datos: Entirely within your Azure sub + chosen LLM endpoint

Sample apps are starting points, not production-ready. Harden auth, rate limiting, and observability before going live.
Keep your OpenAI / LLM keys in Key Vault, not in source code.

Resolución de problemas

Errores comunes y soluciones

azd up fails: no capacity in region

OpenAI capacity varies by region. Try eastus2, swedencentral, or francecentral.

Functions cold-start slowness

Use Premium plan for prod; Consumption is fine for dev but cold-starts stall early chats.

MCP tool call not recognized

Confirm the LangChain.js tool binding is using the MCP client the sample sets up. Check the imports.

Alternativas

mcp-agent-langchainjs vs otros

Alternativa	Cuándo usarla	Contrapartida
Vercel AI SDK starter	You prefer Vercel / Next.js hosting	Different cloud; smaller sample
AWS Bedrock Agents + sample	You're on AWS	Different stack; Bedrock agents aren't MCP-native

Más

Recursos

📖 Lee el README oficial en GitHub

🐙 Ver issues abiertas

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