Real-Time Web Search & News Grounding for LangChain, LlamaIndex & MCP Agents (2026)

import os, requests
from langchain_core.tools import tool

@tool
def google_web_search(query: str, country: str = "us") -> str:
    """Search real-time Google SERP + People Also Ask for fresh factual grounding."""
    r = requests.get(
        "https://api.searchdataapi.com/v1/search",
        params={"q": query, "country": country, "lang": "en", "limit": 5},
        headers={"Authorization": f"Bearer {os.environ['SEARCHDATA_API_KEY']}"},
        timeout=15,
    ).json()["data"]
    lines = [f"[{i['rank']}] {i['title']} ({i['url']}): {i['snippet']}" for i in r["organic_results"]]
    return "\n".join(lines)

import { tool } from "ai";
import { z } from "zod";

export const webSearchTool = tool({
  description: "Query live Google SERP + PAA results",
  parameters: z.object({ query: z.string(), country: z.string().default("us") }),
  execute: async ({ query, country }) => {
    const res = await fetch(
      `https://api.searchdataapi.com/v1/search?q=${encodeURIComponent(query)}&country=${country}&limit=5`,
      { headers: { Authorization: `Bearer ${process.env.SEARCHDATA_API_KEY}` } }
    );
    const { data } = await res.json();
    return data.organic_results;
  },
});

curl -s "https://api.searchdataapi.com/v1/search?q=best+vector+database+for+rag+2026&country=us&lang=en&limit=5" \
  -H "Authorization: Bearer sr_live_your_api_key"

{
  "code": 0,
  "message": "success",
  "data": {
    "query": "best vector database for rag 2026",
    "country": "us",
    "lang": "en",
    "total_results": 10,
    "organic_results": [
      {
        "rank": 1,
        "title": "Best Vector Databases for Production RAG in 2026 — Benchmark",
        "url": "https://supabase.com/blog/vector-database-rag-benchmark",
        "domain": "supabase.com",
        "snippet": "Compare pgvector, Qdrant, Pinecone, and Weaviate across recall@10, p95 query latency, and hybrid BM25 + dense vector indexing."
      },
      {
        "rank": 2,
        "title": "Qdrant vs Pinecone vs pgvector: Choosing a RAG Vector Store",
        "url": "https://qdrant.tech/benchmarks/",
        "domain": "qdrant.tech",
        "snippet": "Filtered HNSW vector search benchmarks at 10M and 100M embeddings with quantization memory footprints."
      }
    ],
    "people_also_ask": [
      {
        "question": "Which vector database is fastest for RAG?",
        "answer": "In-memory Rust engines like Qdrant and Milvus achieve sub-10ms p95 latency at 10M+ vectors, while PostgreSQL pgvector (HNSW) simplifies transactional hybrid search."
      }
    ],
    "related_searches": [
      "pgvector vs qdrant",
      "hybrid search rag pipeline",
      "langchain vector store"
    ]
  },
  "meta": {
    "credits_charged": 2,
    "credits_remaining": 11998
  }
}