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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Usage analytics
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Average 3.8/5 across 9 of 9 tools scored. Lowest: 3.2/5.
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.
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.
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.
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.
answer_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） |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It transparently describes both default behavior (retrieval) and optional behavior (LLM integration), which are the key behavioral traits. No contradictions or omissions.
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 concise sentences front-load the purpose and key behavioral distinction (default vs LLM mode). Every word adds value; no wasted space.
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?
The description adequately covers the tool's behavior given its low complexity. No output schema exists, but the description explains the two return types. All parameters are documented. It is complete for a straightforward Q&A 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 coverage is 100% with descriptions for all parameters. The description adds extra meaning by explaining default behavior and the effect of 'synthesize', going beyond the schema's parameter descriptions.
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 answers insurance knowledge questions, distinguishing it from sibling tools like 'chat' which is more general. It specifies the default mode (knowledge base retrieval) and an optional LLM synthesis mode.
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 explains when to use the 'synthesize' parameter but does not explicitly provide when to use this tool versus alternatives like 'chat' or 'search_products'. Usage context is implied but not fully delineated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
chatBInspect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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It mentions multi-turn capability and guidance suitability but lacks details on session behavior, limitations, or response format.
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 is concise and front-loaded, but could benefit from slightly more structure or key 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?
Adequate for a simple chat tool with output schema missing, but lacks explicit differentiation from siblings like answer_question and details on return values.
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 baseline 3 applies. Description does not add meaning beyond what schema already provides for parameters.
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 it is for natural language multi-turn dialogue, specifically for uncertain information or guidance scenarios, distinguishing it from sibling tools like answer_question. However, it does not explicitly name alternative tools.
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 when to use (uncertain info, guidance scenarios) but does not provide explicit when-not-to-use or alternatives.
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"] |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It lists possible outcomes (normal, extra premium, rejection) but does not state whether the tool is read-only, requires authentication, or has side effects. The behavior is minimally described.
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 concise sentence with no wasted words. It front-loads the purpose and includes key scope details. Every part is necessary.
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 4 parameters, no output schema, and no annotations, the description provides the core purpose and outcome types. However, it lacks details on return format, error handling, or edge cases. It is minimally adequate but not 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 description coverage is 100%, so the baseline is 3. The description does not add meaning beyond the schema; it merely repeats the health_conditions input and outcome types. No additional parameter context is provided.
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 ('评估', assess) and resource ('各产品的可投保性', insurability of each product), with a specific input (health conditions) and possible outcomes. It distinguishes from siblings like 'recommend' and 'compare_products' by focusing on insurability assessment.
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 use when needing to evaluate insurability based on health, but does not explicitly state when to use this tool versus alternatives (e.g., 'recommend' for general product suggestions, 'get_premium' for pricing). No when-not or prerequisite guidance is provided.
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） |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It mentions comparing 2-5 products and returning a structured table, but lacks details on data freshness, performance implications, or error handling. Basic behavioral info is present, but no depth beyond the stated purpose.
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, clear sentence front-loading purpose and key details. No filler; every part 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?
For a comparison tool with no output schema, description covers main purpose, scope (2-5 products), key dimensions, and output type. Missing error handling or behavior for invalid inputs, but still provides sufficient context for correct invocation.
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 context by linking age and gender to precise premium calculation and listing comparison dimensions, which enriches understanding beyond the schema alone.
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 verb (compare), resource (2-5 products), key dimensions (premium, coverage, deductible, renewal), and output (structured comparison table). It effectively distinguishes from siblings like get_product_detail (single product) and search_products (search).
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?
Usage is implied by the description (use when comparing multiple products), but it does not explicitly state when not to use or mention alternatives. No exclusions or when-not-to guidance 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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry behavioral disclosure. It explains compact vs full response but does not mention safety, permissions, or side effects. Since it's a read operation, this is adequate but not comprehensive.
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 main purpose. No redundant or unnecessary 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?
Without an output schema, the description provides reasonable detail on what is returned (structured info, modes). It mentions '条款详情' (clause details) in full mode, but could elaborate on the actual fields. Still, it is fairly complete for a simple fetch 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 coverage is 100% with detailed parameter descriptions. The description adds context for compact mode ('只返回摘要字段，适合Agent快速浏览') but does not significantly exceed schema info. 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 '获取单个产品的结构化信息' (get structured information for a single product), specifying it's for one product and distinguishing from siblings like search_products. It also explains compact mode for quick browsing.
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 provides context on when to use compact=true vs default, noting compact is suitable for 'Agent快速浏览' (Agent quick browsing). However, it does not explicitly exclude other tools or mention 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.
get_schemaAInspect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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries full burden. It discloses output components and that source_url can be directly given to users, but does not mention if the tool is read-only, authorization needs, or other behavioral traits.
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 longer than necessary and includes a call flow that seems more relevant to sibling tools. It could be more focused on what get_schema does.
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, the description adequately explains the return values: field priorities, impacts, default behavior, and source_url. It is sufficient for an agent to understand what to expect.
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 parameter is fully documented in the schema. The description adds little beyond restating that it returns field priorities for each capability.
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 that the tool returns field priorities, missing impacts, and default behaviors for each capability. It distinguishes itself from sibling tools like recommend or search by focusing on metadata about those capabilities.
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 that implies order of usage for other tools but does not explicitly state when to use get_schema itself. It lacks guidance on alternatives or 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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
未提供注释，描述承担全部责任。披露了默认纯数据模式和推理模式延迟、健康条件自动核保、缺失字段提示等行为。
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?
四句中文，先阐明目的再说明关键行为，无冗余，信息密度高。
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?
未输出模式，描述仅提及返回最佳推荐和缺失字段提示，未说明输出结构或格式，对10个参数的组合用例覆盖不足。
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?
90%模式覆盖率下，描述对health_conditions和include_reasoning增加了模式未涵盖的行为说明（核保结论、延迟信息），新增价值明显。
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?
明确说明根据用户画像推荐保险方案，并自称核心工具。与搜索、比较等兄弟工具区分明显。
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?
说明即使信息不全也可调用，以及include_reasoning的用途，但未明确何时替代兄弟工具或何时不应调用。
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_productsAInspect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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description does not state read-only nature, auth requirements, or result limits beyond schema defaults.
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 concise sentences, front-loaded with main purpose and filter options. No redundancy.
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 purpose and filters well for a search tool with 5 optional params. Could mention default limit or behavior with no parameters, but overall adequate.
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 description does not add much beyond the schema parameter descriptions. Baseline 3 applies.
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 searches insurance products by conditions and returns a structured JSON list, with specific filter criteria (category, age, budget, keyword). This distinguishes it from sibling tools like get_product_detail 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?
Implies usage for filtering products, but no explicit guidance on when to use this vs. siblings like recommend or check_underwriting.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
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Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
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For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
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