Pangolinfo Amazon Data MCP
RemoteAbout Pangolinfo Amazon Data MCP
pangolinfo-mcp is a Python client for calling Pangolinfo Amazon Data MCP tools from code, without an AI client. Its synchronous interface covers data from Amazon, WIPO, and PACER.
Key features
- 01Call 19 data tools synchronously
- 02Query Amazon products and reviews
- 03Analyze categories, niches, and bestsellers
- 04Retrieve AI search results and keyword trends
- 05Search WIPO trademarks and PACER data
- 06Call tools by name
Use cases
- 01Look up Amazon products in Python programs
- 02Filter product categories and check bestsellers
- 03Retrieve keyword trends or AI search results
- 04Search trademark and PACER litigation data
MCP support
This project is a Python client for calling 19 tools from the MCP service. It also documents a hosted server for MCP clients and an stdio bridge.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://mcp.pangolinfo.com/mcpclaude mcp add --transport http pangolin-spg-pangolinfo https://mcp.pangolinfo.com/mcp{"mcpServers":{"pangolin-spg-pangolinfo":{"type":"http","url":"https://mcp.pangolinfo.com/mcp"}}}Open in Cursor{"servers":{"pangolin-spg-pangolinfo":{"type":"http","url":"https://mcp.pangolinfo.com/mcp"}}}Open in VS Code{"mcpServers":{"pangolin-spg-pangolinfo":{"type":"http","url":"https://mcp.pangolinfo.com/mcp"}}}{"mcpServers":{"pangolin-spg-pangolinfo":{"serverUrl":"https://mcp.pangolinfo.com/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
https://mcp.pangolinfo.com/mcpAuth requiredTools
Details
From the project READMEpangolinfo-mcp
Python client for the Pangolinfo Amazon Data MCP server — 19 Amazon / WIPO / PACER data tools callable from Python, no AI client required.
Built and maintained by PANGOLIN INFO TECH PTE. LTD.
Why this package
The Amazon Data MCP gives AI agents 19 data tools via the MCP protocol. This package lets Python developers call those same tools from code — no Claude, Cursor, or any AI client needed.
- 19 tools in one client: Amazon product, reviews, search, niches, bestsellers, AI SERP, WIPO, PACER and more
- Zero dependencies beyond
httpx— no MCP SDK required - Sync interface (async coming soon)
- Same engine, same pricing as the REST API and MCP server
Installation
pip install pangolinfo-mcp
Quick start
1. Get a permanent API key
Sign up at tool.pangolinfo.com and grab a permanent API key from Account Center.
2. Use the client
from pangolinfo_mcp import PangolinfoMCPClient
client = PangolinfoMCPClient(api_key="your_permanent_api_key")
# Amazon product detail
product = client.get_amazon_product("B0DYTF8L2W")
print(product["title"])
# Keyword search
results = client.search_amazon("wireless earbuds")
# Best sellers in a category
bestsellers = client.list_bestsellers(category_id="172282")
# WIPO trademark check
trademarks = client.wipo_search("Nike")
# PACER patent litigation
cases = client.pacer_search("Apple Inc.")
# List all available tools
tools = client.list_tools()
client.close()
Context manager
with PangolinfoMCPClient(api_key="your_key") as client:
product = client.get_amazon_product("B0DYTF8L2W")
All 19 tools
Amazon Core Data (5)
| Method | Description |
|---|---|
client.search_amazon(keyword) | Search Amazon products |
client.get_amazon_product(asin) | Full product detail |
client.get_amazon_reviews(asin) | Paginated reviews |
client.list_seller_products(seller_id) | Seller storefront |
client.scrape_url(url) | Scrape any Amazon page |
Category & Niche Analysis (8)
| Method | Description |
|---|---|
client.filter_niches(**metrics) | Blue-ocean niche discovery |
client.filter_categories(**metrics) | Category filtering |
client.search_categories(query) | Full-text category search |
client.get_category_children(node_id) | Browse node children |
client.get_category_tree() | Root category tree |
client.batch_category_paths(category_ids) | Batch category paths |
client.list_bestsellers(category_id) | Best Sellers list |
client.list_new_releases(category_id) | New Releases list |
Search & SERP AI (3)
| Method | Description |
|---|---|
client.ai_search(query) | AI Overview / AI Mode SERP |
client.keyword_trends(keyword) | Keyword trend time-series |
client.search_amazon_alexa(query) | Alexa for Shopping |
Maps & IP Compliance (3)
| Method | Description |
|---|---|
client.search_local_maps(query) | Maps POI data |
client.wipo_search(keyword) | WIPO trademark search |
client.pacer_search(query) | PACER patent litigation |
Utility (1)
| Method | Description |
|---|---|
client.capabilities() | List all live tools (free) |
Generic tool call
# Call any tool by name
result = client.call_tool("search_amazon", {"keyword": "laptop", "page": 2})
Use as an MCP server
This repo also documents the hosted MCP server that any MCP-compatible client (Claude Desktop, Cursor, VS Code, etc.) can connect to directly over Streamable HTTP — no local install required.
Add to your client's mcp.json / MCP config:
{
"mcpServers": {
"pangolinfo": {
"url": "https://mcp.pangolinfo.com/mcp",
"headers": {
"Authorization": "Bearer pgl_your_api_key"
}
}
}
}
Or pass the key inline as a query parameter:
https://mcp.pangolinfo.com/mcp?api_key=pgl_your_api_key
Get a permanent API key at tool.pangolinfo.com. The key is sent as a Bearer token; config is not hot-reloaded, so restart/reconnect the client after changing it.
For MCP clients that require a local stdio process, use the official npm bridge:
PANGOLINFO_API_KEY="your_key" npx -y pangolinfo-amazon-data-mcp
Source and client configuration: github.com/Pangolin-spg/amazon-data-mcp.
Links
- MCP product page: pangolinfo.com/amazon-data-mcp
- Documentation: docs.pangolinfo.com
- Get a free API key: tool.pangolinfo.com
- Source code: github.com/Pangolin-spg/pangolinfo-mcp
License
MIT — © PANGOLIN INFO TECH PTE. LTD.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.