AI Bidding Data Analysis Platform

Problem

Bidding analysis often starts from one business question, such as “who is buying domestic databases,” “who is likely to win this server project,” or “how have prices changed over three years.” The hard part is not the question itself, but translating it into multiple structured queries across bids, companies, markets, and price history.

How It Works

The skill converts natural-language requests into calls against mcp-server.zhiliaobiaoxun.com. Its main capabilities include:
- Bid search: tools like search_bids, query_bids_advanced, and get_bid_detail filter announcements by keywords, region, amount, and time.
- Company analysis: tools such as search_company, find_competitors, and find_potential_bidders cover entity matching, competitors, and likely bidders.
- Market analysis: tools like get_top_purchasers, get_top_suppliers, aggregate_bids_advanced, and get_price_trends support rankings, aggregations, and price trends.

A key step is selecting match_modes correctly, then combining keywords, keyword_groups, and exclude_keywords. For example, use caller to find projects issued by a company, winner to find projects it won, and use query_bids_advanced when AND logic is required. Results are paginated with page and page_size, while error codes guide checks for ZLBX_API_KEY, quota, or invalid parameters.

Boundaries

It works best for defined bidding, winning, procurement, brand, or industry questions. For company websites, news, policy context, or industry-chain analysis, combine with WebSearch as secondary evidence, while treating bid data as primary. Note that quotas and balance limits apply, and complex queries may consume more calls; auto-registration requires explicit user consent.