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WhyLingxi 保险顾问 Insurance Advisor

Server Details

保险产品搜索、推荐、保费试算、核保预检。覆盖65家保司483款产品。China insurance MCP server.

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Status
Unhealthy
Uptime
70.6% over 50 days
Last Tested
Transport
Streamable HTTP
URL
Repository
lpb123/whylingxi-insurance-mcp
GitHub Stars
1
Server Listing
WhyLingxi Insurance MCP Server

TDQS

A4/5.0

Scored across 9 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: answer_question handles factual queries, chat manages multi-turn dialogue, check_underwriting assesses health risks, compare_products compares specific products, get_premium provides precise pricing, get_product_detail retrieves product info, get_schema guides strategy, recommend gives tailored plans, and search_products filters by criteria. No two tools overlap in functionality.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., answer_question, check_underwriting, compare_products, get_premium, get_product_detail). The few single-word tools (chat, recommend) are common exceptions that still convey action clearly.

Tool Count5/5

With 9 tools, the server is well-scoped for an insurance advisor. Each tool serves a necessary function—knowledge retrieval, chat, underwriting, comparison, pricing, details, meta-guidance, recommendation, and search—without redundancy or bloat.

Completeness5/5

The tool set covers the full lifecycle of insurance advisory: product discovery (search, detail, compare), pricing (get_premium), risk assessment (check_underwriting), recommendation (recommend), knowledge support (answer_question), and user guidance (chat, get_schema). No obvious gaps remain for typical use cases.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 9 tool updates
    • First observedanswer_question
    • First observedchat
    • First observedcheck_underwriting
    • First observedcompare_products
    • First observedget_premium
    • First observedget_product_detail
    • First observedget_schema
    • First observedrecommend
    • First observedsearch_products

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