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Documentation Index

Fetch the complete documentation index at: https://docs.prometheusprotocol.org/llms.txt

Use this file to discover all available pages before exploring further.

Welcome! This guide is for anyone who wants to connect an AI application to the powerful, verified services available in the Prometheus App Store. The core problem for any advanced AI is trust. How can your agent safely use a third-party tool without risking data integrity or security? The Prometheus Protocol solves this by providing a decentralized App Store of services that have undergone a rigorous, on-chain verification process. There are two primary ways to connect to these services, depending on your needs.

Using an Existing MCP Client

The most common method. If you use an application that already supports MCP, you just need to find a service and follow the guided setup.

Building a Custom MCP Client

For advanced integration, you can use our TypeScript SDK to build a native MCP client directly into your application.

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Connecting via an Existing Application

Dozens of popular AI applications and developer tools already support the Model Context Protocol (MCP). This includes clients like:
  • Claude (Desktop & Code)
  • Cursor
  • Gemini CLI
  • Raycast
  • VS Code (via extensions)
  • And many more…
For these applications, the process is incredibly simple, as the App Store provides tailored instructions for each one.
1

Discover a Service

Browse the Prometheus App Store to find a service with the tools your agent needs.
2

Click 'Connect'

On the service’s detail page, click the prominent Connect button. This will open a modal window with specific setup instructions.
3

Select Your Application

Inside the modal, you’ll see a series of tabs for different MCP clients (e.g., Cursor, VS Code, Claude Desktop). Select the tab corresponding to your application.
4

Follow the Guided Instructions

The App Store will provide the exact steps and, in most cases, a pre-configured JSON snippet for you to copy and paste directly into your application’s settings.

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Building a Custom MCP Client (Advanced)

If you are developing your own AI agent or application from scratch, you can implement a native MCP client for the deepest level of integration. The @modelcontextprotocol/sdk provides the necessary tools: 
npm install @modelcontextprotocol/sdk
All Prometheus-verified services use a StreamableHTTPClientTransport. The following code snippet is the correct way to establish a connection, including a fallback to the older SSE transport for compatibility. 
import { Client } from '@modelcontextprotocol/sdk/client/index.js';
import { StreamableHTTPClientTransport } from '@modelcontextprotocol/sdk/client/streamableHttp.js';
import { SSEClientTransport } from '@modelcontextprotocol/sdk/client/sse.js';

async function connectToService(url: string) {
  const baseUrl = new URL(url);
  let client: Client | undefined = undefined;

  try {
    // Primary Method: Streamable HTTP
    client = new Client({
      name: 'my-custom-agent',
      version: '1.0.0',
    });
    const transport = new StreamableHTTPClientTransport(baseUrl);
    await client.connect(transport);
    console.log('Connected using Streamable HTTP transport');
  } catch (error) {
    // Fallback Method: SSE
    console.log(
      'Streamable HTTP connection failed, falling back to SSE transport',
    );
    client = new Client({
      name: 'my-custom-agent-sse',
      version: '1.0.0',
    });
    const sseTransport = new SSEClientTransport(baseUrl);
    await client.connect(sseTransport);
    console.log('Connected using SSE transport');
  }

  return client;
}
Once connected, you can use the client object to interact with the service’s tools, prompts, and resources as shown in the MCP TypeScript SDK reference.