eCommerce
underpricedai.com
Provides eBay US secondhand pricing estimates, recent sold comparables, and worth-it verdicts for items, including scan results after background comps retrieval.
ENDPOINT 1
https://underpricedai.com/api/mcp
MCP server metadata
- Name
- underpriced-ai
- Version
- 1.1.0
Underpriced AI prices secondhand items for resellers from real eBay sold listings. Use price_item when a user asks what something is worth or would sell for; give it a photo URL when one exists (marks, sizes and condition are read from the photo) and the description otherwise. Pass the user's cost to get a worth-it verdict. If the user uploaded a photo you cannot pass as a URL, describe it precisely in description: brand, model number, size, marks, condition and what is included; that is what the price rests on. Use get_sold_comps when the user wants the recent sales themselves for a specific item. Prices are USD and reflect eBay US. Quote the range as well as the point: a used item's sale price is a distribution. When the user has signed in through the connector, price_item with a photo runs a real scan on their account (their credits, plan or pay as you go; saved to their history with a link to list it on eBay), and buy_credits can add a pack or set up pay as you go (a card on file, $0.25 per photo scan) for them. A signed-in photo price returns as soon as the estimate exists; when it says sold comps are still loading (account.compsPending), call get_scan_result with account.scanId after about 20 seconds for the final number and the comps. After price_item with a photo on a signed-in account, list_on_ebay prepares the eBay listing from that scan (title, list price, condition, description as a draft on their account) and returns the link where the user reviews and publishes it in the app; it never publishes by itself. Anonymous calls are capped per day; unlimited photo scans and one-step eBay listing are at https://underpricedai.com.
Known tools 6
price_itemWhat a secondhand item sells for on eBay US: a calibrated estimate with a range, the recent real sold comps behind it, three list prices (fast, market, patient) with the 14-day sell chance, the net after eBay fees, and a worth-it verdict when you pass what the user would pay.
Potential side effectsget_sold_compsThe most recent real eBay US sales for a search query (brand, model or pattern, a few specific words), with the count, median and quartiles.
Inferred read-onlyworth_itNet after eBay fees and a verdict (worth it / thin / pass) for an expected sale price and what the user would pay.
Potential side effectsget_scan_resultFor a signed-in user: the finished result for a scan on their account, in the same shape as price_item, once the sold comps have landed in the background.
Inferred read-onlyCONNECT WITH APPROVAL
Client installation
Review this server and its permissions before adding it. Secret placeholders must be set locally.
Codex
~/.codex/config.toml
[mcp_servers.underpriced-ai]
url = "https://underpricedai.com/api/mcp"
enabled = true
Claude Code
.mcp.json
{
"mcpServers": {
"underpriced-ai": {
"type": "http",
"url": "https://underpricedai.com/api/mcp"
}
}
}
Claude Desktop
Settings → Connectors → Add custom connector
Name: underpriced-ai
Remote MCP URL: https://underpricedai.com/api/mcp
Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.
Cursor
.cursor/mcp.json
{
"mcpServers": {
"underpriced-ai": {
"url": "https://underpricedai.com/api/mcp"
}
}
}
Visual Studio Code
.vscode/mcp.json
Add to Visual Studio Code{
"servers": {
"underpriced-ai": {
"type": "http",
"url": "https://underpricedai.com/api/mcp"
}
}
}
Generic MCP
Client-specific MCP configuration
{
"name": "underpriced-ai",
"transport": "streamable-http",
"url": "https://underpricedai.com/api/mcp"
}
MCP Inspector
Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.
TRUST AND VERIFICATION EVIDENCE
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Checking the associated registrable domain. The BuiltWith key remains server-side.
Evidence is source-attributed and does not guarantee that a third-party server is safe. Risk labels are conservative metadata heuristics.