mcpField
O
Orbis Research Mcp

Orbis Research Mcp

Remote
github.com/OrbisAPIData & Analytics2Updated 20w ago
Repository

About Orbis Research Mcp

Orbis Research MCP gives AI agents access to web search, scraping, and data extraction APIs, including Reddit, Google Search, and Maps. Calls are paid per use in USDC via x402 on Base, without API keys or subscriptions.

Key features

  1. 01Keyword web search
  2. 02Search Reddit posts and comments
  3. 03Scrape web pages
  4. 04Extract structured data from pages
  5. 05Retrieve Google Search and Maps results
  6. 06Plan scraping and score data quality

Use cases

  1. 01Search the web and Reddit for research topics
  2. 02Extract structured details such as prices from pages
  3. 03Find places and their reviews
  4. 04Plan a website data extraction project

MCP support

The service exposes research and data extraction API tools to clients through a remote MCP endpoint. Calls are settled per use in USDC via x402 on Base.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://orbisapi.com/api/mcp/research
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

2
ParameterTypeRequiredDescription
searchstringNoWhat Research & Data Extraction API do you need? e.g. "web search"

Details

From the project README

Orbis Research & Data Extraction MCP 🔍

Real web search, scraping, and data extraction APIs for AI agents. Pay per call with USDC on Base — no API keys, no accounts, no subscriptions.

MCP x402

What's Actually In Here

These are the most-called APIs in this domain on Orbis:

APICallsWhat it does
Web Search Query241Keyword web search returning structured results
Reddit Subreddit Search API221Search Reddit posts across any subreddit
Web Content Type Classifier API193Classify what type of content a URL contains
Web Data Extractor Intelligence API193Intelligently extract structured data from any page
Reddit Posts & Comments API180Fetch posts and comments from Reddit
Google Search Results API178Structured Google SERP results
Google Maps & Places API173Places, reviews, and location data from Google Maps
Web Scrape Planner API163Generate a scraping strategy for any website
Structured Data Extractor API58Extract structured JSON from unstructured web pages
Web Scraping API57Render and scrape any public web page
Data Extraction Quality Scorer API50Score the quality of extracted data
Web Scraping ROI Calculator API37Estimate the cost/value of a scraping project

Quick Start

No API key needed. No account. Paste into your MCP client:

Claude Desktop / Claude Code
{
  "mcpServers": {
    "orbis-research": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://orbisapi.com/api/mcp/research"]
    }
  }
}

Config path: ~/Library/Application Support/Claude/claude_desktop_config.json

Cursor / Windsurf / Cline
{
  "mcpServers": {
    "orbis-research": {
      "url": "https://orbisapi.com/api/mcp/research"
    }
  }
}
OpenAI Codex CLI
# ~/.codex/config.yaml
mcpServers:
  orbis-research:
    type: url
    url: "https://orbisapi.com/api/mcp/research"

Example Prompts

Once connected, try:

  • "Search the web for the latest news about Coinbase's x402 integration"
  • "Search Reddit's r/MachineLearning for discussions about MCP servers"
  • "Scrape this page and extract all the pricing data as JSON: https://..."
  • "Get the top 10 Google results for 'best MCP servers 2025'"
  • "Find all Google Maps reviews for this business and summarize sentiment"
  • "Plan how I'd scrape this e-commerce site for product data"

Direct x402 Usage (Node.js)

import { wrapFetchWithPayment } from "x402-fetch";
import { privateKeyToAccount } from "viem/accounts";
import { createWalletClient, http } from "viem";
import { base } from "viem/chains";

const account = privateKeyToAccount(process.env.WALLET_PRIVATE_KEY);
const walletClient = createWalletClient({ account, chain: base, transport: http() });
const fetch = wrapFetchWithPayment(globalThis.fetch, walletClient);

// Google Search — 178 real calls on Orbis
const resp = await fetch(
  "https://orbisapi.com/api/proxy/google-search-results-api-9c77d5/search",
  { method: "POST", headers: { "Content-Type": "application/json" },
    body: JSON.stringify({ query: "MCP servers for AI agents", limit: 10 }) }
);
console.log(await resp.json());

See example.mjs for a full working script.

All Orbis Domain MCPs

DomainURL
₿ Crypto & Blockchainhttps://orbisapi.com/api/mcp/crypto
🔍 Research & Datahttps://orbisapi.com/api/mcp/research
🛒 Commerce & Retailhttps://orbisapi.com/api/mcp/commerce
✈️ Travelhttps://orbisapi.com/api/mcp/travel
🌐 All 20,000+ APIshttps://orbisapi.com/api/mcp

Built by Orbis — the API marketplace for AI agents.

Connect

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "orbis-research": { "url": "https://orbisapi.com/api/mcp/research" }
  }
}
OpenAI Agents SDK

Python:

from agents.mcp import MCPServerStreamableHttp
from agents import Agent, Runner

mcp = MCPServerStreamableHttp(url="https://orbisapi.com/api/mcp/research")

agent = Agent(
    name="Orbis Research Agent",
    model="gpt-4o",
    mcp_servers=[mcp]
)

result = await Runner.run(agent, "Browse available research APIs")
print(result.final_output)

TypeScript:

import { Agent, run } from "@openai/agents";
import { MCPServerStreamableHttp } from "@openai/agents/mcp";

const mcp = new MCPServerStreamableHttp({
  url: "https://orbisapi.com/api/mcp/research",
});

const agent = new Agent({
  name: "Orbis Research Agent",
  model: "gpt-4o",
  mcpServers: [mcp],
});

const result = await run(agent, "Browse available research APIs");
console.log(result.finalOutput);

Calls are billed per-use in USDC via x402 on Base mainnet. No subscription, no API key required.

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.