Claudeに質問する: 「なぜ[カテゴリー]のクエリで、私たちではなく[競合他社]が引用されているのか？」答えを得て、修正方法を知る。
AgentAEOは、AIエージェントのための検索インテリジェンスレイヤーです。Claude、Cursor、およびMCP互換エージェントに対して、どのブランドがAIの引用を獲得しているか、そしてその理由を正確に伝えます。
npx @agentaeo/mcp-server@latest次に、Claude Desktopで以下のように質問します：
「[domain.com]の[カテゴリー]クエリについて、AI引用監査を実行して。代わりに引用されている競合他社と、その理由を教えて。」
これだけです。これで、AIワークフロー内にライブの選択インテリジェンスが組み込まれました。
Related MCP server: ai-visibility-mcp
AgentAEOは、ChatGPT、Perplexity、Claude、Google AI全体で実際の購入者クエリを実行し、以下を返します：
エンジンごとのクエリあたりの引用率
各クエリで勝利している競合他社
推定収益損失額（月額）
AIが貴社をスキップする構造的な理由
コピー＆ペースト可能なスキーマ修正案
SEOに投資しているブランドの60%は、Googleでのランキングが高いにもかかわらず、AI生成の回答では不可視状態です。
これが「選択ギャップ（Selection Gap）」です。AIは貴社のブランドを見つけてはいますが、一貫して選択してはいません。貴社よりも先に競合他社が検討セットに入っています。
AgentAEOはこのギャップを測定し、金額で定量化し、修正案を生成します。
別名：生成エンジン最適化（GEO）、回答エンジン最適化（AEO）、AI SEO — AgentAEOは、AIが貴社と競合他社のどちらを推奨するかを決定する引用レイヤーを測定します。
機能 | AgentAEO | Conductor | Profound | AthenaHQ | seoClarity |
4つのエンジンでのライブ引用テスト | ✅ | ❌ | ❌ | ❌ | ❌ |
リスクのある収益の推定 | ✅ | ❌ | ❌ | ❌ | ❌ |
30日間の修正ロードマップ | ✅ | ❌ | ❌ | ❌ | ❌ |
デプロイ可能なJSON-LDスキーマ | ✅ | ❌ | ❌ | 一部 | ❌ |
llms.txt生成 | ✅ | ❌ | ❌ | ❌ | ❌ |
無料プラン | ✅ | ❌ | ❌ | ❌ | ❌ |
一回限りの診断（サブスクリプション不要） | ✅ | ❌ | ❌ | ❌ | ❌ |
Anthropicレジストリ上のMCPサーバー | ✅ | ✅ | ✅ | ❌ | ✅ |
1. 競合インテリジェンス
「stripe.comの決済処理クエリについて監査して。ChatGPTとPerplexityで、Stripeの代わりに推奨されているフィンテックブランドはどこ？」
2. 自社ブランドのモニタリング
「[your-domain.com]の引用ヘルスチェックを実行して。月間の推定収益損失額と、上位3つの修正案を教えて。」
3. エージェンシー調査
「これら5つのドメインを監査して：[リスト]。検索マーケティングスコア（Retrieval Marketing Score）でランク付けして。競合他社と比較して最も引用ギャップが大きいのはどこ？」
Claude DesktopまたはClaude Cowork内のAgentAEO MCPを使用すれば、手作業で30日かかっていたAEO作業が30分で完了します：
Claudeに質問：
「[domain.com]の完全なAEO監査を実行して。4つのエンジンすべてでバイヤークエリをテストし、引用ギャップを特定し、30日間の修正ロードマップを生成し、JSON-LDスキーマファイルを作成し、llms.txtを書き、リスクのある収益に関する経営層向けの要約を作成して。」
1回のセッションで出力される内容：
エンジンごとのクエリあたりの引用評価
推定収益損失額
優先順位付けされた30日間の修正計画
コピー＆ペースト可能なJSON-LD（FAQPage、HowTo、Organization）
公開準備が整ったllms.txtファイル
ステークホルダー向けの経営層向け要約
公式Anthropicレジストリで動作するMCPサーバーを持つ唯一のAEOプラットフォームです。GlamaでAAA評価を獲得。
→ agentaeo.com → retrieval.marketing → npmの @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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Track your brand's visibility in AI search across Claude, ChatGPT, Perplexity, and Gemini.
Query your brand's AI visibility across ChatGPT, Claude, Perplexity, and Gemini.
