AIパイプラインのための品質管理 — 単一のMCPツール。Claude Code、Claude Desktop、およびあらゆるMCPクライアントで動作します。
チームの29.5%がAI出力の評価を全く行っていません。 (LangChain調査) ナレッジワーカーは週に4.3時間をAI出力の事実確認に費やしています。 (Microsoft 2025)
AgentDesk MCPがこれを解決します。あらゆるAIパイプラインに、30秒で独立した敵対的レビューを追加できます。
npx @ezark-publish/agentdesk-mcpclaude mcp add agentdesk-mcp -- npx @ezark-publish/agentdesk-mcp{
"mcpServers": {
"agentdesk-mcp": {
"command": "npx",
"args": ["-y", "@ezark-publish/agentdesk-mcp"],
"env": { "ANTHROPIC_API_KEY": "sk-ant-..." }
}
}
}リモートアクセス、Smitheryホスティング、またはマルチクライアント設定のためにHTTPサーバーとして実行します：
# Start with HTTP transport on port 3100
MCP_HTTP_PORT=3100 npx @ezark-publish/agentdesk-mcp
# Or use the --http flag (defaults to port 3100)
npx @ezark-publish/agentdesk-mcp --httpMCPエンドポイント: POST http://localhost:3100/mcp
ヘルスチェック: GET http://localhost:3100/health
npm install github:Rih0z/agentdesk-mcpANTHROPIC_API_KEY 環境変数 (独自のキーを使用 — BYOK)
Related MCP server: open-code-review
review_outputAIが生成したあらゆる出力に対する敵対的品質レビュー。独立したレビュアーが作成者にミスがあることを前提として、積極的に問題を探します。
入力:
パラメータ | 必須 | 説明 |
| はい | レビュー対象のAI生成出力 |
| いいえ | カスタムレビュー基準 |
| いいえ | カテゴリ: |
| いいえ | レビュアーモデル (デフォルト: |
出力:
{
"verdict": "PASS | FAIL | CONDITIONAL_PASS",
"score": 82,
"issues": [
{
"severity": "high",
"category": "accuracy",
"description": "Claim about X is unsupported",
"suggestion": "Add citation or remove claim"
}
],
"checklist": [
{
"item": "Factual accuracy",
"status": "pass",
"evidence": "All statistics match cited sources"
}
],
"summary": "Overall assessment...",
"reviewer_model": "claude-sonnet-4-6"
}review_dual二重敵対的レビュー — 2人の独立したレビュアーが異なる視点から出力を評価し、マージエージェントが結果を統合します。
どちらか一方のレビュアーが重大な問題を発見した場合 → 統合判定は FAIL となります
低い方のスコアを採用します
すべての問題を統合し、重複を排除します
品質が極めて重要な高リスクの出力に使用してください。
パラメータは review_output と同じです。
敵対的プロンプト: レビュアーはミスがあることを前提とするよう指示されます。疑わしきは罰する姿勢です。
証拠に基づくチェックリスト: すべての PASS 項目には具体的な証拠が必要です。証拠のない項目は自動的に FAIL に格下げされます。
不正防止バリデーション: チェックリスト項目の30%以上に証拠がない場合、レビュー全体が強制的に FAIL となり、スコアは最大50に制限されます。
構造化された出力: 判定 + 数値スコア + カテゴリ化された問題 + チェックリスト (単なる「良さそう」という評価ではありません)。
コードレビュー: バグ、セキュリティ問題、パフォーマンス問題のチェック
コンテンツレビュー: 正確性、読みやすさ、SEO、ターゲット適合性の検証
事実確認: AI生成テキスト内の主張の妥当性検証
翻訳品質: 正確さと自然さのチェック
データ抽出: 完全性と正確性の検証
あらゆるAI出力: 要約、レポート、提案書、メールなど
自己レビューには体系的な寛容バイアスがあります。自身の出力をレビューするLLMは、エラーを生み出したのと同じ盲点を共有しています。研究によると、モデルはハルシネーション（幻覚）を起こしている時ほど、自信に満ちた言語を使用する傾向が34%高いことが示されています。
AgentDeskは、敵対的プロンプトを用いた別のレビュアーの呼び出しを使用しており、自己レビューとは根本的に異なります。
機能 | AgentDesk MCP | 手動プロンプト | Braintrust | DeepEval |
ワンツール設定 | はい | いいえ | いいえ | いいえ |
敵対的レビュー | はい | DIY | いいえ | いいえ |
二重レビュアー | はい | DIY | いいえ | いいえ |
不正防止バリデーション | はい | いいえ | いいえ | いいえ |
SDK不要 | はい | はい | いいえ | いいえ |
MCPネイティブ | はい | いいえ | いいえ | いいえ |
プロンプトインジェクション: すべての「LLM-as-judge（判定者としてのLLM）」システムと同様に、敵対的な入力がレビュアーの判定を操作しようとする可能性があります。不正防止バリデーション層は表面的な不正を緩和しますが、執拗な敵対的入力は依然として課題です。高リスクのユースケースでは、決定論的なバリデーションと組み合わせてください。
BYOKコスト: review_output の呼び出しごとに1回のLLM API呼び出しが発生し、review_dual では3回発生します。これをパイプラインのコストに含めてください。
HTTP統合を好むチーム向けに、追加機能（エージェントマーケットプレイス、コンテキスト学習、ワークフロー）を備えたホスト型REST APIを agentdesk.usedevtools.com で提供しています。
git clone https://github.com/Rih0z/agentdesk-mcp.git
cd agentdesk-mcp
npm install
npm test # 35 tests
npm run buildMIT
Built by EZARK Consulting | Web Version
Execute a service on the AgentDesk marketplace. Requires an AgentDesk API key for authentication. Pass service-specific input parameters.
| Name | Required | Description | Default |
|---|---|---|---|
| service_id | Yes | Service ID to execute (e.g., "review", "web_scrape", "realtime_jp", "pdf_generate", "summarize", "classify") | |
| input | Yes | Service-specific input parameters | |
| api_key | No | BYOK: Your Anthropic API key (for AI-powered services like review) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions only an authentication requirement (API key) but does not describe side effects (e.g., whether executing a service modifies state), idempotency, rate limits, or error conditions. The description is insufficient for an agent to understand what happens when the tool is invoked.
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 very concise at two sentences with no wasted words. However, it could be more informative within the same length by clarifying the service execution context or referencing the sibling tools. The front-loading is reasonable but the brevity sacrifices completeness.
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 annotations and output schema, the description is insufficiently complete. It does not explain return values, error handling, or how to properly use the api_key parameter. For a tool with nested objects and no output schema, more context is needed to guide the agent effectively.
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% as all parameters have descriptions. The description adds minimal value: it notes 'service-specific input parameters' but does not elaborate on how to structure the 'input' object for different service IDs. The api_key parameter is described in the schema as 'BYOK: Your Anthropic API key', while the description mentions an 'AgentDesk API key', causing slight inconsistency. Overall, the description does little beyond what the schema already provides.
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 ('Execute a service') and the resource ('AgentDesk marketplace'), providing a specific verb+resource pair. However, it does not distinguish this tool from its siblings (list_services, review_dual, review_output), which could lead to confusion about when to use this generic service execution tool versus those specialized 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 mentions requiring an API key and passing service-specific input, but provides no guidance on when to use this tool versus alternatives like list_services or review_dual. There is no mention of prerequisites (e.g., selecting a service from list_services first) or when not to use this tool. The phrase 'service-specific input parameters' lacks detail on how to determine which parameters are appropriate for a given service_id.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available services on the AgentDesk marketplace. Returns service catalog with pricing, quality scores, and capabilities. Filter by category, minimum quality score, maximum price, or capability.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Filter by category: quality_assurance, web_scraping, realtime_data, document_generation, text_processing | |
| min_score | No | Minimum quality score (0-100) | |
| max_price | No | Maximum price per call in USD | |
| capability | No | Filter by capability keyword |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behaviors; it mentions returning service details but omits pagination, rate limits, or any restrictions. It is 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?
The description is two sentences with no wasted words: first sentence states purpose, second adds return content and filters. Extremely concise.
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 simple listing tool with no output schema and no annotations, the description covers the basics but lacks details on pagination, error handling, or output structure, which might be needed.
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?
Input schema has 100% description coverage, and the description merely lists the filter names without adding meaning beyond what the schema already provides, so it meets the 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?
The description clearly states the verb 'List' and resource 'available services on the AgentDesk marketplace', and distinguishes from siblings by focusing on browsing the catalog, while sibling tools execute or review services.
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 for browsing services with filters, but does not explicitly state when to use this tool versus alternatives like execute_service or review tools, nor provides exclusions or conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Dual adversarial review: two independent reviewers assess the output from different angles, then a merge agent combines their findings. Stricter than single review — if either reviewer finds a critical issue, the merged verdict is FAIL. Use for high-stakes outputs where quality is critical.
| Name | Required | Description | Default |
|---|---|---|---|
| output | Yes | The AI-generated output to review (max 100K chars) | |
| criteria | No | Custom review criteria | |
| review_type | No | Review category label | |
| model | No | Reviewer model ID (default: claude-sonnet-4-6) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavior. It explains the dual review process and the merge verdict logic, but omits details like side effects, authentication needs, or output structure, leaving gaps for an agent.
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 two sentences: the first explains the process, the second provides usage guidance and the verdict rule. No extraneous words, highly 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?
Given 4 parameters, no output schema, and no annotations, the description adequately explains the core functionality but does not cover output format, error handling, or prerequisites, leaving room for improvement.
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?
With 100% schema description coverage, the schema already documents all parameters. The description adds no additional meaning beyond what is in the schema, so it does not improve understanding of parameter usage.
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 explicitly states the tool performs a dual adversarial review with two independent reviewers and a merge agent. It clearly distinguishes itself from the sibling tool 'review_output' by being stricter and specifying the verdict rule.
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 advises using this tool for high-stakes outputs and contrasts it with single review, but does not explicitly state when not to use it or list alternatives beyond the implied single review.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Adversarial quality review of any AI-generated output. An independent reviewer assumes the author made mistakes and actively looks for problems. Returns structured verdict (PASS/FAIL/CONDITIONAL_PASS), score (0-100), categorized issues with severity, and evidence-based checklist. Works for any output type: code, content, summaries, translations, data extraction, etc.
| Name | Required | Description | Default |
|---|---|---|---|
| output | Yes | The AI-generated output to review (max 100K chars) | |
| criteria | No | Custom review criteria — what specifically to check for | |
| review_type | No | Review category label (e.g., "code", "content", "factual", "translation") | |
| model | No | Reviewer model ID (default: claude-sonnet-4-6) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral transparency. It describes the output structure (verdict, score, issues) but does not disclose any potential side effects, destructive actions, authentication needs, or rate limits. The 'adversarial' nature is mentioned but not elaborated.
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 concise at two sentences, front-loading the core purpose and then detailing the output. Every sentence adds value; no redundant or verbose phrasing.
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 effectively explains the return values (verdict, score, issues, checklist). It covers the tool's broad applicability and key inputs. Minor omissions: it does not clarify that 'criteria' is optional or describe defaults for 'review_type' and 'model'.
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%—all four parameters have descriptions in the schema. The description does not add additional meaning beyond the schema; it only summarizes the output format. Baseline score of 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 it performs an adversarial quality review of AI-generated output, using specific verbs ('review') and a resource type ('output'). It does not differentiate from the sibling tool 'review_dual', suggesting both may perform reviews, so it misses the top score for sibling distinction.
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 for quality checking of any AI output, but provides no explicit guidance on when to use this tool versus alternatives (e.g., 'review_dual') or when not to use it. It lacks clear context for exclusion or alternative selection.
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.
v1.3.0execute_servicelist_servicesreview_dualreview_outputScored across 4 tools
The four tools are clearly divided into two distinct categories: marketplace services (execute_service, list_services) and output review (review_dual, review_output). Even within the review category, the two tools have well-differentiated purposes (single vs. dual adversarial review), so there is no ambiguity.
All tool names follow a consistent verb_noun pattern in snake_case: execute_service, list_services, review_dual, review_output. The naming is predictable and easy to understand.
With 4 tools, the server covers two distinct functions. While each function could benefit from more tools (e.g., more marketplace operations or review management), the current count is reasonable for a focused server and does not feel excessive or insufficient.
The marketplace side only offers list and execute, lacking create, update, or delete operations for services. The review side provides two types of reviews but no ability to list or manage past reviews. These gaps limit the server's coverage for its implied domain.
Adversarial behavioural-bias engine — audits your decisions for cognitive biases via your own AI.
Expert review for AI agents. On-chain proof of human review.
Agentic code review, no signup to try: reality gates + frontier-model review, with veto.
Devil's-advocate QC API for AIs: post a decision, get strongest counter-argument. 0.1 USDT/call
