- One MCP URL gives an assistant listings, prices and reviews to read.
- The model that reasons is yours; the server only fetches.
- Every tool prices itself in its description, so an agent can budget.
- Long scans hand back a scan id instead of a timeout.
AI assistants retrieving listings, not guessing
Ask an assistant about a product category and, without a tool, it answers from training data. That data is months old and has never seen this week's prices. With AI assistants retrieving listings through a tool, the answer rests on what the marketplace shows today.
Connect the hosted server at mcp.nooticr.com/mcp and the assistant discovers the tools by itself. It can scan an Amazon category, open one Temu product, or pull the reviews for a Flipkart listing. Then it reads them in the same conversation where it knows your product.
- scan_amazon_category and scan_marketplace_category for a whole category.
- get_amazon_product and get_marketplace_product for one listing.
- A free view that draws the model's read beside the listings it cites.
Why the model should be yours
Many marketplace tools still run their own model and hand you its verdict. You cannot ask it why, and you cannot see which reviews it skipped. Here the server returns material, and the model you already trust forms the conclusion.
That suits AI power users who switch models, compare them, or run their own. The tool surface is model-agnostic. A frontier model, a local one, or an agent you wrote all see the same listings.
Jungle Scout, Helium 10, SmartScout and Keepa now offer MCP servers too, for Amazon sales estimates, keywords and price history. What Nooticr returns instead is review text across 11 marketplaces, with social comments beside it.
Built for agents that run unattended
An agent needs to know what a call costs before it makes one. Every tool states its credit price in its description, and a scan confirms its total before spending. A failed call is refunded automatically.
Collection is slow because each listing is a real browser render. So a scan returns what is ready plus a scan id, and polling that id is free. An agent loop can wait for the rest without paying twice or timing out.
Tip:Tell the agent to poll until the scan says it is finished before it concludes anything. Half a category reads like a whole one.
Generative AI projects that need real product data
Generative AI projects built on product data — a listing writer, a pricing assistant, a research agent — need fresh, structured inputs. The same tools return typed JSON alongside the text, so a pipeline can read fields rather than parse prose.
AI researchers use it the same way: a reproducible fetch of listings and reviews, with the source id on every item, fed to whichever model is under test.
Frequently Asked Questions
Which AI assistants work with Nooticr?
Any MCP client: Claude, Claude Code, ChatGPT, Cursor and others. Use the hosted connector at https://mcp.nooticr.com/mcp, or run it locally with npx -y @nooticr/mcp.
Does Nooticr run its own AI on the listings?
No. The tools fetch listings, prices and reviews and hand them to your assistant. Your model reads them and forms the conclusion, so you can question it against the evidence.
Do I pay for the AI tokens through Nooticr?
No. Nooticr bills credits for each fetch. Your model's tokens are billed by your model's provider, as a separate line.