ai.summarize

# 1. Probe the endpoint with no auth — receive 402 with PaymentRequirements
curl -sS -X POST 'https://2s.io/api/ai/summarize' \
  -H 'Content-Type: application/json' \
  -d '{"url":"https://example.com","instruction":"example"}'

# 2. Sign the EIP-3009 transferWithAuthorization for the advertised price +
#    payTo from the 402 envelope, then retry with PAYMENT-SIGNATURE:
curl -sS -X POST 'https://2s.io/api/ai/summarize' \
  -H 'Content-Type: application/json' \
  -H 'PAYMENT-SIGNATURE: <base64-json-payload>' \
  -d '{"url":"https://example.com","instruction":"example"}'

# Or just use the canonical runner — it handles the whole loop:
#   EVM_PRIVATE_KEY=0x... node --env-file=.env.local \
#     --experimental-strip-types scripts/x402-pay.ts \
#     'https://2s.io/api/ai/summarize'

import { TwoS } from '@2sio/sdk'

const client = new TwoS({
  privateKey: process.env.EVM_PRIVATE_KEY as `0x${string}`,
})

const result = await client.ai.summarize({
  "url": "https://example.com",
  "instruction": "example"
})

console.log('endpoint:', result.endpoint)
console.log('cost:', result.costUsd, 'USDC')
console.log('tx:', result.settlement?.txHash)
console.log('data:', result.data)

import os
from twosio import TwoS

client = TwoS(private_key=os.environ["EVM_PRIVATE_KEY"])

result = client.ai.summarize(url="https://example.com", instruction="example")

print("endpoint:", result.endpoint)
print("cost:", result.cost_usd, "USDC")
print("tx:", (result.settlement or {}).get("tx_hash"))
print("data:", result.data)

// 1. Add @2sio/mcp to your MCP host config (Claude Desktop example below).
//    EVM_PRIVATE_KEY funds x402 payments per call.

// claude_desktop_config.json
{
  "mcpServers": {
    "2sio": {
      "command": "npx",
      "args": ["-y", "@2sio/mcp"],
      "env": { "EVM_PRIVATE_KEY": "0x..." }
    }
  }
}

// 2. Once the server is running, agents call this tool via standard MCP:

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "ai.summarize",
    "arguments": {
      "url": "https://example.com",
      "instruction": "example"
    }
  }
}

# pip install langchain-twosio langchain-anthropic
import os
from langchain_twosio import get_twosio_tools
from langchain_anthropic import ChatAnthropic
from langgraph.prebuilt import create_react_agent

tools = get_twosio_tools(private_key=os.environ["EVM_PRIVATE_KEY"])
llm = ChatAnthropic(model="claude-haiku-4-5", temperature=0)
agent = create_react_agent(llm, tools)

# The agent will pick "twosio_ai_summarize" when the prompt matches.
result = agent.invoke({
    "messages": [("user", "...prompt that requires ai.summarize...")]
})
print(result["messages"][-1].content)

# pip install llama-index-tools-twosio llama-index-llms-anthropic
import asyncio, os
from llama_index_tools_twosio import get_twosio_tools
from llama_index.llms.anthropic import Anthropic
from llama_index.core.agent.workflow import FunctionAgent

tools = get_twosio_tools(private_key=os.environ["EVM_PRIVATE_KEY"])
llm = Anthropic(model="claude-haiku-4-5")
agent = FunctionAgent(tools=tools, llm=llm)

# The agent will pick "twosio_ai_summarize" when the prompt matches.
response = asyncio.run(agent.run("...prompt that requires ai.summarize..."))
print(response)