papers.search

# 1. Probe with no auth → 402 envelope with PaymentRequirements
curl -sS 'https://2s.io/api/papers/search?q=example&limit=10&since=2024-01-01&sources=example'

# 2. Sign + retry with PAYMENT-SIGNATURE:
curl -sS 'https://2s.io/api/papers/search?q=example&limit=10&since=2024-01-01&sources=example' \
  -H 'PAYMENT-SIGNATURE: <base64-json-payload>'

# Or use the canonical runner (handles probe → sign → retry):
#   EVM_PRIVATE_KEY=0x... node --env-file=.env.local \
#     --experimental-strip-types scripts/x402-pay.ts \
#     'https://2s.io/api/papers/search?q=example&limit=10&since=2024-01-01&sources=example'

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

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

const result = await client.papers.search({
  "q": "example",
  "limit": 10,
  "since": "2024-01-01",
  "sources": "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.papers.search(q="example", limit=10, since="2024-01-01", sources="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": "papers.search",
    "arguments": {
      "q": "example",
      "limit": 10,
      "since": "2024-01-01",
      "sources": "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_papers_search" when the prompt matches.
result = agent.invoke({
    "messages": [("user", "...prompt that requires papers.search...")]
})
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_papers_search" when the prompt matches.
response = asyncio.run(agent.run("...prompt that requires papers.search..."))
print(response)

{
  "ok": true,
  "items": [
    {}
  ],
  "total": 1,
  "source": {
    "provider": "example",
    "url": "example",
    "license": "example"
  },
  "meta": {
    "query": "example",
    "sources": [
      "example"
    ],
    "errors": [
      {
        "source": "example",
        "message": "example"
      }
    ],
    "attribution": [
      {
        "source": "example",
        "license": "example",
        "url": "example"
      }
    ]
  }
}