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.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.7/5 across 9 of 9 tools scored. Lowest: 2.9/5.
Each tool has a clearly distinct purpose: answer_question for factual Q&A, chat for multi-turn conversation, check_underwriting for health-based assessment, compare_products for side-by-side comparison, get_premium for precise pricing, get_product_detail for structured product info, get_schema for meta-guidance, recommend for personalized recommendation, and search_products for filtering. No overlap.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., answer_question, check_underwriting, compare_products). The naming is predictable and readable.
With 9 tools, the set is well-scoped for an insurance advisor. It covers Q&A, chat, underwriting, comparison, premium, product details, meta-information, recommendation, and search—neither too few nor too many.
The tool surface is comprehensive for insurance advisory: knowledge base, conversational, underwriting assessment, product comparison, premium calculation, product details, operational guidance, recommendation engine, and search. No obvious gaps.
Available Tools
9 toolsanswer_questionAInspect
回答保险知识问题。默认返回知识库检索结果；传synthesize:true可获得LLM整合答案。
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | 问题 | |
| product_id | No | （可选）针对某款产品的问题 | |
| synthesize | No | 是否调LLM整合答案（默认false，纯检索模式更快） |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses two operation modes and implies faster retrieval for default. However, it omits details like required authentication, rate limits, or what happens when product_id is provided. Adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two efficient sentences in Chinese, front-loaded with purpose, then mode distinction. No redundant information. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and 3 parameters, description covers key usage but lacks detail on return format or response structure. Adequate for a simple tool but could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for all parameters. The description adds slight value by clarifying the default behavior of synthesize, but this is already in the schema. No additional meaning beyond schema explanations.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states tool answers insurance knowledge questions. Specifies two modes (default retrieval, LLM-synthesized). Distinguishes from sibling tools like chat, compare_products, etc. which have different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Describes default behavior and how to get integrated answers via the synthesize parameter. Provides clear usage context but does not explicitly state when not to use or list alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
chatAInspect
自然语言多轮对话（适合信息不确定、需要引导的场景，或人类用户直接使用）。
| Name | Required | Description | Default |
|---|---|---|---|
| message | Yes | 用户消息 | |
| session_id | No | 会话ID，传入可保持多轮对话上下文 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, description mentions multi-turn context via session_id but no details on rate limits, authorization, or response behavior. Adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with parenthetical usage guidance, no wasted words, front-loaded with purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Simple tool with 2 parameters and no output schema; description covers purpose, usage, and session context adequately. Lacks output details but acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with parameter descriptions; the description adds minimal extra meaning beyond the schema, achieving baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states this tool performs natural language multi-turn dialogue, suitable for uncertain information and guidance scenarios, differentiating it from specialized siblings like answer_question or recommend.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides when to use (uncertainty, guidance, human users), implying not for simple factual queries, but lacks explicit 'when not to use' statements.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_underwritingAInspect
核保预判：根据用户健康状况，评估各产品的可投保性（可正常投保/需加费除外/可能拒保）。
| Name | Required | Description | Default |
|---|---|---|---|
| age | No | 年龄（影响可选产品范围） | |
| category | No | 想投保的险种（不传则评估医疗+重疾+意外+定寿） | |
| product_ids | No | 指定产品ID列表（不传则自动选取各险种Top产品评估） | |
| health_conditions | Yes | 健康状况列表，如["高血压2级","甲状腺结节3类","乙肝小三阳"] |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only states the basic function and outcomes, without mentioning safety (read-only nature), authorization needs, rate limits, or result format. This is insufficient for a tool that processes sensitive health data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that immediately establishes the tool's purpose and expected outcomes. Every word is meaningful, with no redundancy or unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and moderate complexity (4 parameters), the description provides a high-level overview but omits details like result structure, handling of multiple conditions, and interpretation of outcomes. It is functional but leaves gaps for an AI agent to fully understand behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% parameter description coverage, so the baseline is 3. The description adds no additional detail beyond the schema; it merely restates the use of health conditions. It does not clarify relationships between parameters or provide usage examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the verb '评估' (evaluate) and the resource '各产品的可投保性' (insurability of each product). It distinguishes from sibling tools like 'recommend' or 'compare_products' by focusing on risk evaluation based on health conditions, with explicit outcome categories.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when assessing insurability given health conditions, but lacks explicit guidance on when to use it versus alternatives, such as when to prefer 'recommend' or 'compare_products'. No 'when not to use' or alternative tool names are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compare_productsAInspect
对比2-5款产品的关键维度（保费、保障、免赔额、续保等），返回结构化对比表。
| Name | Required | Description | Default |
|---|---|---|---|
| age | No | 用于查询精确保费的年龄 | |
| gender | No | 用于查询精确保费的性别 | |
| product_ids | Yes | 产品ID列表（2-5个） |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the output as a structured comparison table with specific dimensions (premium, coverage, etc.) and implies a read-only operation. It does not mention authentication or rate limits, which is acceptable for a query tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is front-loaded with the core action and key details. Every part serves a purpose with no superfluous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description adequately covers purpose, scope (2-5 products), and output format (structured table). It explicitly mentions the range and key dimensions. Could mention the requirement for at least 2 product IDs, but the schema requires non-empty array.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already explains each parameter. The description adds context about the comparison but does not provide additional meaning beyond what the schema offers (e.g., age and gender for premium calculation). Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'compare' and the resource 'products' with a specific scope (2-5 products) and key dimensions (premium, coverage, deductible, renewal). It distinguishes itself from siblings like get_product_detail (single product) and search_products.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when comparing multiple products but does not explicitly state when to use versus alternatives like get_premium or recommend. No 'when not' or exclusion criteria are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_product_detailAInspect
获取单个产品的结构化信息。compact=true时只返回摘要字段，适合Agent快速浏览；默认返回完整信息含条款详情。
| Name | Required | Description | Default |
|---|---|---|---|
| compact | No | 精简模式：只返回核心字段+coverage前8项摘要，省略条款/FAQ详情 | |
| product_id | Yes | 产品ID |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses the behavioral difference between compact and full modes, including what is omitted in compact mode. No annotations exist, so description adequately covers read-only behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers the main behavior and parameter modes. Lacks return format details but acceptable given no output schema and simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema provides detailed parameter descriptions (100% coverage). The tool description adds usage context for the compact parameter, enhancing understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves structured information for a single product, distinguishing it from sibling tools like 'search_products' and 'compare_products'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides context on when to use compact mode (for quick browsing) versus full mode, but does not explicitly compare to other tools or give exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_schemaBInspect
获取调用策略和字段优先级。调用流程：(1)收集用户age后即可调recommend(2)有health_conditions时自动附带核保结论(3)include_reasoning默认false,Agent自行组织语言(4)返回的source_url可直接给用户作为投保链接。本工具返回各能力的字段优先级、缺失影响、默认行为。
| Name | Required | Description | Default |
|---|---|---|---|
| capability | No | 想了解哪个能力，不传则返回全部 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It states the tool returns field priorities and default behavior, but does not explicitly declare it as read-only, safe, or free of side effects. The mention of source_url relates to recommend's output, not the tool's own behavior. Partial transparency but lacks a clear safety profile.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately concise but mixes an overview with a specific call flow for recommend. Some details, like the numbered list, might be better placed in the recommend tool's description. It is not overly long but could be more focused on the tool's own functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter and no output schema, the description explains what the tool returns (field priorities, missing impact, default behavior) but does not specify the output format. It is adequate for basic understanding but lacks full completeness regarding the structure of the returned data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with a clear enum and description for the capability parameter. The description adds significant context beyond the schema by explaining the tool returns field priorities and outlining an invocation flow. This extra meaning helps the agent understand how to apply the parameter results.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it retrieves invocation strategy and field priority for capabilities, which is a specific verb-resource pair. It differentiates from sibling tools like recommend or search_products by returning metadata about those tools. However, the name 'get_schema' might imply returning an actual schema, but the description focuses on strategic behavior, so there is a slight mismatch.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes a call flow for recommend, implying when to use the tool's output (e.g., after collecting user age). However, it does not explicitly state when to use this tool versus alternatives, nor does it provide clear guidance on when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recommendAInspect
根据用户画像推荐保险方案（核心工具）。信息不全也可调用——返回最佳推荐+缺失字段提示。传入health_conditions自动附带核保结论。默认纯数据模式(<100ms)，设include_reasoning:true生成自然语言理由(+3s)。
| Name | Required | Description | Default |
|---|---|---|---|
| age | Yes | 年龄（必填） | |
| needs | No | 需求列表，如["重疾保障","医疗报销","意外防护"] | |
| budget | No | 年预算（元） | |
| gender | No | ||
| family_role | No | 被保人角色 | |
| occupation_class | No | 职业类别(1-6类) | |
| existing_coverage | No | 已有保障，如["百万医疗","意外险"] | |
| health_conditions | No | 既往症，如["高血压2级","甲状腺结节"] | |
| include_reasoning | No | 是否生成LLM推荐理由（默认false，Agent调用无需开启；设true适用于直接面向人类展示） | |
| has_social_security | No | 是否有社保 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
无注释，但描述详细披露了行为：返回最佳推荐+缺失字段提示、health_conditions自动附带核保结论、默认纯数据模式速度<100ms、设置include_reasoning会增加3秒延迟。覆盖了关键性能和安全考虑。
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
两句话包含了核心功能、用法条件、特殊行为、性能模式，无冗余信息。关键信息前置（核心工具、信息不全可用）。
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
对于10个参数的推荐工具，描述覆盖了主要使用场景和关键参数行为。但未说明输出格式或如何解释推荐结果（无输出模式），略有缺失。
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
输入模式覆盖率为90%，大多数参数已有基本描述。描述额外解释了health_conditions和include_reasoning的行为，超越了模式定义。预算、年龄等参数未进一步细化，但整体补偿到位。
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
描述明确指出工具推荐保险方案，并自称'核心工具'，与兄弟工具（如compare_products, search_products）区别明确。动词'推荐'直接说明功能。
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
明确说明即使信息不全也可调用，并解释返回值行为。给出了include_reasoning的使用场景。但未提及何时不应使用本工具而使用其他工具（如compare_products）的对比。
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_productsCInspect
按条件搜索保险产品，返回结构化JSON列表。支持按险种、年龄、预算、关键词筛选。
| Name | Required | Description | Default |
|---|---|---|---|
| age | No | 被保险人年龄 | |
| limit | No | 最多返回条数，默认10 | |
| budget | No | 年预算上限（元） | |
| keyword | No | 产品名或保险公司关键词 | |
| category | No | 险种：医疗险/重疾险/意外险/定期寿险/终身寿险/年金险/旅游险/团险 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavior. It only says it returns a structured JSON list, with no mention of side effects, permission requirements, rate limits, or pagination. The implied read-only nature is clear, but details like default limit (from schema) are not mentioned.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no wasted words, purpose stated first. Could be improved by structuring with separate lines or clarifying output structure, but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 5 parameters, no output schema, and no annotations, the description is too minimal. Missing details on search behavior (e.g., fuzzy matching, ordering, response format) and does not cover the limit parameter. Incomplete for a search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% and all parameters are already described. The description lists the same filtering criteria (category, age, budget, keyword) without adding new semantics. Baseline 3 is appropriate as description adds minimal value beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool searches insurance products and returns structured JSON. It lists filtering criteria (insurance type, age, budget, keyword), distinguishing it from siblings like get_product_detail (single product) and recommend (recommendations). However, it does not explicitly differentiate from compare_products or check_underwriting.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The description only states what it does, not when it is appropriate or when not to use it (e.g., compared to recommend or get_product_detail).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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