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

Por que usar

Principais recursos

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 ao vivo

Como fica na prática

agent-langchainjs.replay ▶ pronto

0/0

Instalar

Escolha seu 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
    }
  }
}

Abra Claude Desktop → Settings → Developer → Edit Config. Reinicie após salvar.

~/.cursor/mcp.json · .cursor/mcp.json

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

Cursor usa o mesmo esquema mcpServers que o Claude Desktop. Config de projeto vence a global.

VS Code → Cline → MCP Servers → Edit

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

Clique no ícone MCP Servers na barra lateral do Cline, depois "Edit Configuration".

~/.codeium/windsurf/mcp_config.json

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

Mesmo formato do Claude Desktop. Reinicie o Windsurf para aplicar.

~/.continue/config.json

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

O Continue usa um array de objetos de servidor em vez de um map.

~/.config/zed/settings.json

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

Adicione em context_servers. Zed recarrega automaticamente ao salvar.

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

Uma linha só. Verifique com claude mcp list. Remova com claude mcp remove.

Casos de uso

Usos do mundo real: mcp-agent-langchainjs

Bootstrap a serverless agent on Azure with MCP tool calls

👤 Azure devs building AI features ⏱ ~120 min advanced

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

Pré-requisitos

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

Fluxo

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.

Armadilhas

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

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

Fluxo

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.

Combinações

Combine com outros MCPs para 10× de alavancagem

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

Ferramentas

O que este MCP expõe

Ferramenta	Entradas	Quando chamar	Custo
(reference app — not a callable MCP)	N/A	This is a sample app you deploy, not a tool Claude calls	N/A

Custo e limites

O que custa rodar

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

Segurança

Permissões, segredos, alcance

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

Saída de dados: 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.

Solução de problemas

Erros comuns e correções

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. outros

Alternativa	Quando usar	Troca
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

Mais

Recursos

📖 Leia o README oficial no GitHub

🐙 Ver issues abertas

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