Ask Claude or ChatGPT to “find road tenders in Gujarat closing this month” with nothing connected, and you will get an answer. It will be fluent, specific, formatted into a neat table — and largely invented. Tender IDs that don’t resolve. Departments that don’t procure that category. Closing dates pulled from nowhere.

This is not a flaw you can prompt your way out of. Language models are genuinely good at reasoning over procurement text: reading a 60-page NIT, comparing eligibility clauses, spotting a restrictive pre-qualification criterion. They are structurally incapable of fetching a tender that was published four hours ago on a state PWD portal. Nothing in their training data contains it.

The fix is to give the model a real data source it can query. That is what MCP does, and it is why a tender assistant that actually works looks less like a chatbot and more like a connected workspace.

This guide covers connecting the Tenderkart MCP server — 180+ Indian tender sources, 25,000+ tenders a day — to Claude, ChatGPT, Perplexity, Cursor, Claude Code, and Codex.