Custom client (Python / Node)

pip install mcp

import asyncio
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.session import ClientSession

async def main():
    headers = {"Authorization": "Bearer trodo_mk_xxx"}
    async with streamablehttp_client(
        "https://mcp.trodo.ai/mcp",
        headers=headers,
    ) as (read, write, _):
        async with ClientSession(read, write) as session:
            await session.initialize()
            tools = await session.list_tools()
            for t in tools.tools:
                print(t.name, "—", t.description[:80])

            # Call one
            result = await session.call_tool(
                "get_top_users",
                arguments={"metric": "events", "window_days": 7, "limit": 5},
            )
            for c in result.content:
                if c.type == "text":
                    print(c.text)

asyncio.run(main())

npm install @modelcontextprotocol/sdk

import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js";
import { Client } from "@modelcontextprotocol/sdk/client/index.js";

const transport = new StreamableHTTPClientTransport(
  new URL("https://mcp.trodo.ai/mcp"),
  { headers: { Authorization: `Bearer ${process.env.TRODO_MCP_API_KEY}` } },
);
const client = new Client({ name: "my-app", version: "1.0.0" }, { capabilities: {} });

await client.connect(transport);
const tools = await client.listTools();
console.log(tools.tools.map((t) => t.name));

const result = await client.callTool({
  name: "get_run_metrics",
  arguments: { window_days: 30 },
});
console.log(result.structuredContent);

curl -s https://mcp.trodo.ai/mcp \
  -H "Authorization: Bearer trodo_mk_xxx" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'

{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "content": [{ "type": "text", "text": "<one-line summary>" }],
    "structuredContent": { "summary": "...", "rows": [...], ... },
    "isError": false
  }
}