API Documentation

const { Anthropic } = require('@anthropic-ai/sdk'); const anthropic = new Anthropic({ apiKey: 'your-key' }); // Get Claude-formatted tool definitions (FREE) const toolsResp = await fetch('https://x402.rugmunch.io/tools/anthropic-tools'); const { tools } = await toolsResp.json(); // Call Claude with tools const message = await anthropic.messages.create({ model: "claude-sonnet-4-20250514", max_tokens: 1024, messages: [ { role: "user", content: "Is this wallet suspicious? Gix4P9AmwcZRGzr2hCEME5m2QAvY86dBfm8c7e7MpFzv" } ], tools: tools }); // Execute tool calls for (const content of message.content) { if (content.type === "tool_use") { const result = await fetch(`https://rugmunch.io/api/v1/x402-tools/${content.name}`, { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify(content.input) }).then(r => r.json()); console.log(result); } }

const { GoogleGenAI } = require('@google/genai'); const ai = new GoogleGenAI({ apiKey: 'your-key' }); // Get Gemini-formatted function declarations (FREE) const toolsResp = await fetch('https://x402.rugmunch.io/tools/gemini-tools'); const { function_declarations } = await toolsResp.json(); // Call Gemini with function calling const response = await ai.models.generateContent({ model: "gemini-2.5-pro", contents: "Scan 6p6xgHyF7AeE6TZkSmFsko444wqoP15icUSqi2jfGiPN for rug patterns", config: { tools: [{ function_declarations }] } }); // Execute function calls for (const part of response.candidates[0].content.parts) { if (part.functionCall) { const result = await fetch(`https://rugmunch.io/api/v1/x402-tools/${part.functionCall.name}`, { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify(part.functionCall.args) }).then(r => r.json()); console.log(result); } }

from langchain_openai import ChatOpenAI from langchain.agents import create_react_agent from langchain.tools import Tool import requests # Get LangChain tool definitions (FREE) tools_data = requests.get("https://x402.rugmunch.io/tools/langchain-tools").json() tools = tools_data["tools"] # Create custom tool wrappers def call_rmi_tool(tool_name, **kwargs): method = "POST" if tool_name in ["audit", "wallet", "rugshield"] else "GET" resp = requests.request( method, f"https://rugmunch.io/api/v1/x402-tools/{tool_name}", json=kwargs if method == "POST" else None, params=kwargs if method == "GET" else None ) return resp.json() # Create LangChain tools rmi_tools = [ Tool(name=tool["name"], func=lambda **kw, name=tool["name"]: call_rmi_tool(name, **kw), description=tool["description"]) for tool in tools ] # Create ReAct agent llm = ChatOpenAI(model="gpt-4o") agent = create_react_agent(llm, rmi_tools) result = agent.invoke({"input": "Check if token is safe to buy"}) print(result["output"])

# Install MCP SDK pip install mcp # Run the x402 MCP server python -m app.mcp.x402_mcp_server # Configure Claude Desktop (claude_desktop_config.json) { "mcpServers": { "rmi-x402-tools": { "command": "python", "args": ["/srv/rmi/backend/app/mcp/x402_mcp_server.py"], "env": { "RMI_BACKEND_URL": "https://rugmunch.io" } } } } # Programmatic MCP client from mcp.client import ClientSession async with ClientSession("python", ["-m", "app.mcp.x402_mcp_server"]) as session: tools = await session.list_tools() print([t.name for t in tools]) result = await session.call_tool("rug_shield", { "address": "6p6xgHyF7AeE6TZkSmFsko444wqoP15icUSqi2jfGiPN", "chain": "solana" }) print(result)