Give Claude live web access with two tool definitions — QuanticData + the Claude API https://quanticdata.io/blog/how-to-use-claude-api/

qd_claude_tools.py

"""A Claude tool loop with real web access, in one file.

    pip install anthropic requests
    export ANTHROPIC_API_KEY=sk-ant-...  QD_API_KEY=qd_live_...
    python3 qd_claude_tools.py "What changed in the EU AI Act timeline this month?"

Two tools is enough: search to FIND, scrape to READ. A search costs $0.0005 and a page
$0.0002, so a six-source turn is about a thousandth of what the model tokens cost.

https://quanticdata.io/web-data-api-for-ai/  ·  https://quanticdata.io/blog/how-to-use-claude-api/
Prefer no plumbing at all? https://github.com/quantumproxies/quanticdata-mcp-server
"""
import json
import os
import sys

import requests
from anthropic import Anthropic

MODEL = os.environ.get("ANTHROPIC_MODEL", "claude-sonnet-5")
BASE = "https://api.quanticdata.io/v1"
QD = {"Authorization": f"Bearer {os.environ['QD_API_KEY']}"}

TOOLS = [
    {
        "name": "search",
        "description": ("Search the live web and get organic results with title, link and "
                        "snippet. Finds pages; does not return their content."),
        "input_schema": {
            "type": "object",
            "properties": {"query": {"type": "string"},
                           "country": {"type": "string", "description": "ISO code, e.g. us"}},
            "required": ["query"],
        },
    },
    {
        "name": "scrape",
        "description": ("Read one URL as clean Markdown, boilerplate removed. Pass `query` to "
                        "get only the passages relevant to a question."),
        "input_schema": {
            "type": "object",
            "properties": {"url": {"type": "string"}, "query": {"type": "string"}},
            "required": ["url"],
        },
    },
]


def run_tool(name, args):
    if name == "search":
        p = requests.post(f"{BASE}/serp", headers=QD, timeout=120,
                          json={"query": args["query"], "country": args.get("country"),
                                "num": 10}).json().get("payload", {})
        return json.dumps([{k: r.get(k) for k in ("position", "title", "link", "snippet")}
                           for r in (p.get("organic") or [])])

    body = {"url": args["url"], "format": "markdown", "contentMode": "article",
            "max_tokens": 1500}
    if args.get("query"):
        body |= {"query": args["query"], "highlights": True}
    p = requests.post(f"{BASE}/scrape", headers=QD, json=body, timeout=180).json().get("payload", {})
    highlights = p.get("highlights")
    text = "\n\n".join(h.get("text", "") for h in highlights) if highlights else p.get("markdown", "")
    return f"source: {args['url']}\n\n{text}"


client = Anthropic()
messages = [{"role": "user", "content": " ".join(sys.argv[1:]) or "What is an MCP server?"}]
system = ("You have live web access. Search to find sources, scrape to read them, and cite the "
          "URL for every factual claim. If the tools did not answer, say so rather than guessing.")

for _ in range(8):
    response = client.messages.create(model=MODEL, max_tokens=4096, system=system,
                                      tools=TOOLS, messages=messages)
    messages.append({"role": "assistant", "content": response.content})

    for block in response.content:
        if block.type == "text" and block.text.strip():
            print(block.text)

    calls = [b for b in response.content if b.type == "tool_use"]
    if not calls:
        break
    messages.append({"role": "user", "content": [
        {"type": "tool_result", "tool_use_id": c.id, "content": run_tool(c.name, c.input)}
        for c in calls
    ]})