问 Claude：“为什么在 [类别] 查询中，AI 引用的是 [竞争对手] 而不是我们？”获取答案。获取解决方案。
AgentAEO 是 AI 代理的检索智能层。它能告诉 Claude、Cursor 以及任何兼容 MCP 的代理，哪些品牌正在赢得 AI 引用，以及具体原因。
npx @agentaeo/mcp-server@latest然后在 Claude Desktop 中询问：
“针对 [domain.com] 的 [类别] 查询运行 AI 引用审计。告诉我哪些竞争对手被引用了，以及原因。”
就是这样。您现在可以在 AI 工作流中获得实时的选择智能。
Related MCP server: ai-visibility-mcp
AgentAEO 在 ChatGPT、Perplexity、Claude 和 Google AI 上运行真实的买家查询，并返回：
每个引擎针对每个查询的引用率
每个查询中胜出的竞争对手
收入流失估算（美元/月）
AI 跳过您的具体结构性原因
可直接复制粘贴的 Schema 修复方案
尽管在 Google 上排名很高，但 60% 投资 SEO 的品牌在 AI 生成的答案中却处于隐形状态。
这就是“选择差距”（Selection Gap）：AI 能找到您的品牌，但并不总是选择它。竞争对手在您之前进入了考虑范围。
AgentAEO 衡量这一差距。以美元量化它。生成修复方案。
也称为：生成式引擎优化 (GEO)、答案引擎优化 (AEO)、AI SEO —— AgentAEO 衡量决定 AI 是否推荐您或竞争对手的引用层。
功能 | AgentAEO | Conductor | Profound | AthenaHQ | seoClarity |
跨 4 个引擎的实时引用测试 | ✅ | ❌ | ❌ | ❌ | ❌ |
风险收入估算 | ✅ | ❌ | ❌ | ❌ | ❌ |
30 天修复蓝图 | ✅ | ❌ | ❌ | ❌ | ❌ |
可直接部署的 JSON-LD schema | ✅ | ❌ | ❌ | 部分 | ❌ |
llms.txt 生成 | ✅ | ❌ | ❌ | ❌ | ❌ |
免费层级 | ✅ | ❌ | ❌ | ❌ | ❌ |
一次性诊断（无需订阅） | ✅ | ❌ | ❌ | ❌ | ❌ |
Anthropic 注册表上的 MCP 服务器 | ✅ | ✅ | ✅ | ❌ | ✅ |
1. 竞争情报
“审计 stripe.com 的支付处理查询。ChatGPT 和 Perplexity 推荐了哪些金融科技品牌而不是 Stripe？”
2. 自有品牌监控
“对 [your-domain.com] 进行引用健康检查，并给我每月收入流失估算以及前 3 大修复建议。”
3. 代理机构研究
“审计这 5 个域名：[列表]。按检索营销评分（Retrieval Marketing Score）对它们进行排名。哪个品牌与其竞争对手相比引用差距最大？”
通过 Claude Desktop 或 Claude Cowork 中的 AgentAEO MCP，过去需要 30 天的手动 AEO 工作现在只需 30 分钟即可完成：
问 Claude：
“对 [domain.com] 进行完整的 AEO 审计。测试所有 4 个引擎上的买家查询，识别引用差距，生成 30 天修复蓝图，创建 JSON-LD schema 文件，编写 llms.txt，并给我一份董事会级别的风险收入摘要。”
单次会话输出：
每个引擎针对每个查询的引用评分
收入流失估算
优先级的 30 天修复计划
可复制粘贴的 JSON-LD (FAQPage, HowTo, Organization)
准备发布的 llms.txt 文件
给利益相关者的执行摘要
这是唯一在官方 Anthropic 注册表上拥有可用 MCP 服务器的 AEO 平台。在 Glama 上获得 AAA 评级。
→ agentaeo.com → retrieval.marketing → npm 上的 @agentaeo/mcp-server
Check status of an AEO audit. Poll until free_preview_ready (free) or is_complete at full report (paid). If paid_pipeline_pending is true, keep polling.
| Name | Required | Description | Default |
|---|---|---|---|
| auditId | Yes | The audit ID returned from run_aeo_audit |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's polling behavior, including conditions for free vs. paid audits and handling of pending states. However, it doesn't mention error handling, timeout limits, or authentication requirements, leaving some behavioral aspects unspecified.
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 extremely concise and front-loaded, consisting of just two sentences that efficiently convey the tool's purpose and critical usage guidelines. Every word earns its place, with no redundant information or unnecessary elaboration.
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 tool's polling complexity and lack of annotations/output schema, the description does well by explaining the polling logic and conditions. However, it doesn't describe what the status response looks like or potential error states, which would be helpful for an agent to interpret results. The completeness is good but not perfect for a polling 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?
The schema description coverage is 100%, with the single parameter 'auditId' well-documented in the schema as 'The audit ID returned from run_aeo_audit'. The description doesn't add any additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 for high schema coverage.
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 'check' and resource 'status of an AEO audit', making the purpose specific. It distinguishes from siblings by focusing on audit status rather than content suite operations (like generate_aeo_content_suite) or running audits (run_aeo_audit).
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 explicitly provides usage guidance: 'Poll until free_preview_ready (free) or is_complete at full report (paid). If paid_pipeline_pending is true, keep polling.' This tells the agent when to use this tool (for polling audit status) and provides specific conditions for continuing polling, which is crucial for understanding its role versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Poll Content Suite generation. After generate_aeo_content_suite returns (HTTP 202), call every 15–30s until status is completed or failed. Same X-API-Key as generate.
| Name | Required | Description | Default |
|---|---|---|---|
| orderId | Yes | orderid returned from generate_aeo_content_suite |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses key behavioral traits: polling nature, timing interval (15-30s), continuation until terminal states (completed/failed), and authentication requirement (same X-API-Key as generate). However, it doesn't mention rate limits, error handling, or what 'completed' or 'failed' entail. Good but not exhaustive.
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, zero waste. First sentence states purpose and polling cadence. Second sentence adds auth detail. Front-loaded with essential info (polling, timing, termination). Every sentence earns its place by providing critical usage context.
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?
No output schema, so description should ideally cover return values, but it doesn't. However, given the tool's simplicity (1 param, no annotations), the description is largely complete: purpose, usage, auth, and parameter context are clear. Minor gap in not explaining status outcomes (what 'completed' or 'failed' mean), but overall sufficient for agent use.
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% (orderId documented), so baseline is 3. The description adds value by explaining orderId's origin ('returned from generate_aeo_content_suite'), providing context beyond schema. It doesn't detail format or constraints, but the linkage to generate is helpful. Scores above baseline for meaningful addition.
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's purpose: 'Poll Content Suite generation' - a specific verb (poll) and resource (Content Suite generation). It distinguishes from siblings by referencing generate_aeo_content_suite and contrasting with download_aeo_content_suite_zip (polling vs. downloading). The description goes beyond the name/title to explain what polling entails.
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?
Explicit guidance on when to use: 'After generate_aeo_content_suite returns (HTTP 202), call every 15–30s until status is completed or failed.' Clear timing, prerequisites, and termination conditions. It distinguishes from check_aeo_audit_status by focusing on Content Suite rather than audit, and from download_aeo_content_suite_zip by specifying polling precedes downloading.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Download the Content Suite ZIP after status is completed (same AGENTAEO_API_KEY as generate). Saves to cwd or AGENTAEO_MCP_DOWNLOAD_DIR.
| Name | Required | Description | Default |
|---|---|---|---|
| orderId | Yes | orderid UUID from generate_aeo_content_suite / check_aeo_content_suite_status | |
| outputFileName | No | Optional filename, e.g. content-stripe.zip (default: content-suite-<first8ofuuid>.zip) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool saves files to specific directories (cwd or AGENTAEO_MCP_DOWNLOAD_DIR) and requires the same API key as 'generate', which adds useful context. However, it doesn't mention potential errors, file size limits, or authentication requirements beyond the API key reference.
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 appropriately concise with two sentences that each serve a clear purpose: first stating the action and prerequisites, second describing the output behavior. It could be slightly more structured but wastes no 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?
For a download tool with no annotations and no output schema, the description provides adequate but minimal context. It covers the prerequisite state and output location, but doesn't explain what happens on failure, file format details, or return values. Given the tool's relative simplicity, this is minimally sufficient.
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 schema already fully documents both parameters. The description doesn't add any meaningful parameter semantics beyond what's in the schema descriptions, so it meets the baseline of 3.
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 action ('Download') and resource ('Content Suite ZIP'), and distinguishes it from siblings by referencing the 'generate' tool. However, it doesn't explicitly differentiate from 'check_aeo_content_suite_status' which is about status checking rather than downloading.
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 clear context for when to use this tool ('after status is **completed**') and references the prerequisite 'generate' tool. It doesn't explicitly state when NOT to use it or name alternatives, but the context is sufficiently clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Start Content Suite generation (HTML + JSON-LD + llms.txt) for a completed audit — async (returns in seconds with orderId). Poll check_aeo_content_suite_status every 15–30s until completed (often 5–25+ min). Uses AGENTAEO_API_KEY — no shell/curl. Admin QA without Cashfree: adminContentBypass=true + allowlisted key. Otherwise pass orderId after payment.
| Name | Required | Description | Default |
|---|---|---|---|
| auditId | Yes | Completed audit id (e.g. aud_xxx_timestamp) | |
| packageType | No | Content bundle type | full |
| orderId | No | UUID from aeo_content_orders after $499 payment. Required when adminContentBypass is false. | |
| adminContentBypass | No | If true: omit orderId; server creates aeo_content_orders (admin/allowlisted key + X-AgentAEO-Admin-Content). For internal QA only. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well: it discloses the async behavior (returns in seconds with orderId), typical completion time (5-25+ min), polling interval (15-30s), authentication requirement (AGENTAEO_API_KEY), and admin bypass conditions. It doesn't mention error handling or rate limits, but covers key operational 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 appropriately sized and front-loaded with the core purpose. Every sentence adds value: async behavior, polling instructions, authentication, and admin bypass details. It could be slightly more structured but avoids 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?
Given the complexity (async operation with polling) and no annotations/output schema, the description is mostly complete: it covers purpose, usage, behavior, and parameter context. It lacks details on error responses or output format, but provides enough for an agent to use the tool effectively with the schema.
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 adds some context: it clarifies that orderId comes from 'aeo_content_orders after $499 payment' and that adminContentBypass requires 'allowlisted key + X-AgentAEO-Admin-Content', but doesn't provide additional meaning beyond what's in the schema for auditId or packageType.
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's purpose: 'Start Content Suite generation (HTML + JSON-LD + llms.txt) for a completed audit.' It specifies the exact output format and the required input condition (completed audit), and distinguishes it from siblings by mentioning the async nature and the need to poll check_aeo_content_suite_status.
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 explicit guidance on when to use this tool vs alternatives: it mentions using adminContentBypass for admin QA without Cashfree, otherwise requiring orderId after payment. It also specifies the polling mechanism with check_aeo_content_suite_status and distinguishes from run_aeo_audit by focusing on content generation post-audit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Start an AEO audit for a URL (async). Returns auditId immediately. Then call check_aeo_audit_status every 10–15s until is_complete or free_preview_ready (free tier stops at step 2).
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The website URL to audit (e.g. https://example.com) | |
| keyword | No | Primary industry keyword; defaults from domain if omitted | |
| tier | No | Audit tier: free (8 queries) or paid (40 queries) | free |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: the operation is asynchronous ('async'), returns an auditId immediately, and has tier-based limitations (free tier stops at step 2). It also implies rate limits or polling intervals ('every 10–15s'). However, it doesn't detail error handling, timeouts, or authentication needs, leaving some gaps.
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 highly concise and well-structured: two sentences that front-load the core action and immediately follow with essential usage instructions. Every sentence earns its place by providing critical operational details without redundancy or fluff.
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 tool's complexity (async operation with polling) and lack of annotations or output schema, the description is largely complete. It covers the async nature, return value (auditId), polling requirements, and tier limitations. However, it doesn't explain what 'step 2' entails or potential errors, leaving minor gaps for an agent to handle edge cases.
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 schema already documents all parameters (url, keyword, tier) with descriptions and defaults. The description adds no additional parameter semantics beyond what's in the schema, such as explaining keyword selection impact or tier implications in more depth. Thus, it meets the baseline but doesn't enhance understanding.
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's purpose: 'Start an AEO audit for a URL (async).' It specifies the action ('Start'), resource ('AEO audit'), and scope ('for a URL'), distinguishing it from sibling tools like check_aeo_audit_status or generate_aeo_content_suite by focusing on initiating an audit rather than checking status or generating content.
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 explicit usage guidance: it instructs to 'call check_aeo_audit_status every 10–15s until is_complete or free_preview_ready' and notes that 'free tier stops at step 2.' This gives clear when-to-use context (start audit, then poll) and distinguishes from alternatives by implying this is the entry point for audits, with check_aeo_audit_status as the follow-up.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.7check_aeo_audit_statuscheck_aeo_content_suite_statusdownload_aeo_content_suite_zipgenerate_aeo_content_suiterun_aeo_auditScored across 5 tools
Each tool has a distinct, non-overlapping purpose: run_aeo_audit initiates an audit, check_aeo_audit_status monitors it, generate_aeo_content_suite starts content generation, check_aeo_content_suite_status monitors that, and download_aeo_content_suite_zip retrieves the result. The descriptions clearly differentiate these workflows, leaving no ambiguity for an agent to misselect tools.
All tool names follow a consistent snake_case pattern with clear verb_noun structure (e.g., run_aeo_audit, check_aeo_audit_status, generate_aeo_content_suite). The naming is predictable and uniform across all five tools, making them easily readable and systematic.
With 5 tools, this server is well-scoped for managing AEO audits and content suites. Each tool serves a specific role in the workflow, from initiation to monitoring to download, and none appear redundant or unnecessary, fitting the domain appropriately without being too sparse or bloated.
The tool set provides complete coverage for the AEO audit and content suite lifecycle: it includes tools to start audits (run_aeo_audit), monitor their status (check_aeo_audit_status), generate content (generate_aeo_content_suite), monitor that generation (check_aeo_content_suite_status), and download results (download_aeo_content_suite_zip). There are no obvious gaps, enabling agents to handle the entire process from start to finish.
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