Grounding chatbots with real-time news: stop stale and hallucinated current events

Ask an LLM what happened in the news this morning and you'll get a confident answer that's stale, vague, or invented. Its training stopped months ago — it has no idea what's breaking right now. The fix isn't a bigger model. It's grounding answers in a live news feed at query time. Open it in your browser: api.newsagentdata.com/public/breaking → You'll see the current breaking headlines as live JSON, each already carrying an urgency_score (0–10), political_lean, topic_tags, country_tags and an event cluster_id — the exact shape of data you'll hand your model, with no NLP pipeline of your own. Prefer the terminal? curl https://api.newsagentdata.com/public/breaking A language model only knows what it was trained on. Anything after its cutoff — today's news — is missing or hallucinated with total confidence. For any assistant that touches current affairs, markets, politics or safety, that's a real risk. Retrieval-augmented generation (RAG): when a user asks something time-sensitive, fetch the relevant recent articles and inject them into the prompt as context, so the model answers from real, dated sources instead of memory. curl -H "X-API-Key: YOUR_KEY" \ "https://api.newsagentdata.com/v1/search?q=USER_TOPIC&days=2" Every article comes pre-scored and classified — urgency_score (0–10), political_lean, topic_tags, country_tags, a timestamp, and an event cluster_id. So the model can rank what matters, cite the source and time, show balanced framing, and avoid repeating one event forty times. Coverage is deepest for English and Russian, and it reads ~3,000 public Telegram channels on top of RSS — sources web-only tools miss. Retrieve on-demand for a chatbot, or keep a vector store current with a webhook / SSE stream so new articles arrive within ~60 seconds. Either way the model never answers from stale memory. The free tier is a real one — 100 requests/day of the full enriched schema, no credit card: 🔑 Free API key: https://newsagentdata.com/signup/?plan=free 🗺️ See coverage live (world map): https://newsagentdata.com/sources/map/ 📚 Docs & full field list: https://newsagentdata.com/documentation/ The MCP server lets an agent query news as a tool, no glue code: / newsagent-mcp NewsAgent Data — MCP server Query scored, classified Russian & English news from any MCP client (Claude Desktop, Cursor, Cline, etc.). The server wraps the NewsAgent Data API — every article it returns is already scored for urgency (0–10), classified by political lean, topic, country and audience, and de-duplicated. Tools Tool What it does get_feed Filtered feed — by country, topic, language, political lean, audience, min urgency, date range search_news Full-text keyword search across the archive get_breaking Recent high-urgency news (urgency ≥ 7 by default) coverage_stats Live totals — articles, sources, countries, languages (no key) list_sources Source catalog metadata (Standard tier+) Setup Get a free API key: https://newsagentdata.com/signup/?plan=free Install deps: pip install -r requirements.txt Add to your MCP client config. Claude Desktop (claude_desktop_config.json): { "mcpServers": { "newsagent": { "command": "python" "args": ["/absolute/path/to/newsagent_mcp.py"], "env": { "NEWSAGENT_API_KEY": … View on GitHub Building something and need higher volume or custom coverage? Message us on Telegram — @NewsAgentDatabot. Grounding sharply reduces but doesn't fully eliminate hallucination — keep the citations visible so users can verify. Russian and English are the deepest-enriched languages. Originally published on NewsAgent Data.