Build with AI Front DoorIntegrate. Extend. Deploy.

curl -X GET https://api.example.com/endpoint \
  -H "Authorization: Bearer exJhbGciOiJSUz324sdfInR5cCI6IkpXVCJ9..."

npm install @anthropic-ai/sdk @anthropic-ai/mcp-client

import Anthropic from "@anthropic-ai/sdk";
import { MCPClient } from "@anthropic-ai/mcp-client";

const AI_FRONT_DOOR_TOKEN = process.env.AIFD_TOKEN!;

// 1. Connect to the AI Front Door MCP server (CoreMethods is exposed here)
const mcpClient = new MCPClient({
  transport: "http",
  url: "https://api.aifrontdoor.com/mcp",
  headers: {
    Authorization: `Bearer ${AI_FRONT_DOOR_TOKEN}`,
  },
});

await mcpClient.connect();

// 2. Fetch available tools from MCP
const { tools } = await mcpClient.listTools();

// 3. Create the Anthropic client
const anthropic = new Anthropic();

// 4. Run the agent loop
async function runAgent(userMessage: string) {
  const messages: Anthropic.MessageParam[] = [
    { role: "user", content: userMessage },
  ];

  while (true) {
    const response = await anthropic.messages.create({
      model: "claude-sonnet-4-6",
      max_tokens: 4096,
      tools,        // pass MCP tools directly to Claude
      messages,
    });

    // If Claude wants to call a tool, execute it via MCP
    if (response.stop_reason === "tool_use") {
      const toolResults: Anthropic.MessageParam = {
        role: "user",
        content: [],
      };

      for (const block of response.content) {
        if (block.type === "tool_use") {
          console.log(`Calling tool: ${block.name}`, block.input);

          const result = await mcpClient.callTool({
            name: block.name,
            arguments: block.input as Record<string, unknown>,
          });

          (toolResults.content as Anthropic.ToolResultBlockParam[]).push({
            type: "tool_result",
            tool_use_id: block.id,
            content: JSON.stringify(result),
          });
        }
      }

      messages.push({ role: "assistant", content: response.content });
      messages.push(toolResults);
      continue;
    }

    // Final text response
    const textBlock = response.content.find((b) => b.type === "text");
    return textBlock?.type === "text" ? textBlock.text : "";
  }
}

// Example usage
const answer = await runAgent(
  "What are the eligibility requirements for unfair dismissal claims?"
);
console.log(answer);

await mcpClient.disconnect();

# .env
AIFD_TOKEN=eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9...
ANTHROPIC_API_KEY=sk-ant-...

curl https://api.aifrontdoor.com/api/knowledge \
  -H "Authorization: Bearer YOUR_BEARER_TOKEN"

curl https://api.aifrontdoor.com/api/knowledge/unfair-dismissal-eligibility \
  -H "Authorization: Bearer YOUR_BEARER_TOKEN"

async function getKnowledge(slug: string, token: string) {
  const res = await fetch(
    `https://api.aifrontdoor.com/api/knowledge/${slug}`,
    { headers: { Authorization: `Bearer ${token}` } }
  );
  if (!res.ok) throw new Error(`Knowledge fetch failed: ${res.status}`);
  return res.json();
}

const item = await getKnowledge("unfair-dismissal-eligibility", token);
console.log(item.title, item.summary);

curl -X POST https://api.aifrontdoor.com/a2a/tasks \
  -H "Authorization: Bearer YOUR_BEARER_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "task": "Summarise the general protections process for a small business employer",
    "context": {
      "userRole": "employer",
      "employeeCount": 12
    }
  }'

{
  "taskId": "task_01J...",
  "status": "completed",
  "result": {
    "summary": "Under the general protections provisions...",
    "sources": [
      { "title": "General Protections Guide", "slug": "general-protections-guide" }
    ]
  }
}