Most AI agents hardcode which tool to use for each query type. MCP changes this by letting agents discover tools at runtime and select the best one based on context. This tutorial builds a routing agent that connects to Scavio's MCP server, discovers 11 search tools, and dynamically picks the optimal platform for each query. No if-else routing logic required: the LLM makes the routing decision based on tool descriptions.
Prerequisites
- Node.js 18+ installed
- @modelcontextprotocol/sdk package
- A Scavio API key from scavio.dev
- An Anthropic or OpenAI API key for the LLM
Walkthrough
Step 1: Install the MCP SDK
Add the MCP client SDK to your project.
Bash
npm install @modelcontextprotocol/sdkStep 2: Connect to the MCP server and discover tools
Create a client that connects to Scavio's MCP server and lists all available tools.
JavaScript
import { Client } from '@modelcontextprotocol/sdk/client/index.js';
import { StreamableHTTPClientTransport } from '@modelcontextprotocol/sdk/client/streamableHttp.js';
const transport = new StreamableHTTPClientTransport(
new URL('https://mcp.scavio.dev/mcp'),
{ requestInit: { headers: { 'x-api-key': process.env.SCAVIO_API_KEY } } }
);
const client = new Client({ name: 'routing-agent', version: '1.0.0' });
await client.connect(transport);
const { tools } = await client.listTools();
console.log(`Discovered ${tools.length} tools:`);
tools.forEach(t => console.log(` - ${t.name}: ${t.description}`));Step 3: Convert MCP tools to LLM tool definitions
Map the discovered MCP tools into the format your LLM expects for tool calling.
JavaScript
function mcpToLlmTools(mcpTools) {
return mcpTools.map(t => ({
type: 'function',
function: {
name: t.name,
description: t.description,
parameters: t.inputSchema
}
}));
}
const llmTools = mcpToLlmTools(tools);Step 4: Build the routing loop
Send user queries to the LLM with the tool list. The LLM picks the tool; your code executes it via MCP.
JavaScript
async function routingAgent(userQuery) {
const messages = [{ role: 'user', content: userQuery }];
const response = await llm.chat({
messages,
tools: llmTools,
tool_choice: 'auto'
});
if (response.tool_calls) {
for (const call of response.tool_calls) {
const result = await client.callTool({
name: call.function.name,
arguments: JSON.parse(call.function.arguments)
});
messages.push({ role: 'tool', content: JSON.stringify(result), tool_call_id: call.id });
}
const final = await llm.chat({ messages, tools: llmTools });
return final.content;
}
return response.content;
}Python Example
Python
# Python MCP client with routing:
import requests, os, json
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
# Simplified routing without full MCP SDK:
PLATFORM_MAP = {
'product': 'amazon', 'price': 'amazon', 'buy': 'walmart',
'video': 'youtube', 'tutorial': 'youtube', 'how to': 'youtube',
'reddit': 'reddit', 'community': 'reddit', 'opinion': 'reddit',
}
def route_search(query: str) -> str:
q_lower = query.lower()
platform = 'google'
for keyword, p in PLATFORM_MAP.items():
if keyword in q_lower:
platform = p
break
resp = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'platform': platform, 'query': query}, timeout=10)
return json.dumps({'platform': platform, 'results': resp.json().get('organic', [])[:3]}, indent=2)JavaScript Example
JavaScript
// Full MCP routing example in the steps above.
// Simplified version without MCP SDK:
const PLATFORM_MAP = { product: 'amazon', video: 'youtube', reddit: 'reddit', opinion: 'reddit' };
async function routeSearch(query) {
let platform = 'google';
for (const [kw, p] of Object.entries(PLATFORM_MAP)) {
if (query.toLowerCase().includes(kw)) { platform = p; break; }
}
const resp = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST', headers: {'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json'},
body: JSON.stringify({platform, query})
});
return {platform, results: (await resp.json()).organic?.slice(0, 3)};
}Expected Output
JSON
An MCP routing agent that discovers search tools dynamically and lets the LLM select the optimal platform per query context.