Open Strategy Partnersの実績ある手法に基づき、テクニカルマーケティングコンテンツの作成、最適化、製品ポジショニングのための包括的なツールスイートです。これは、OSPマーケティングツール向けMCPサーバーのTypeScript実装です。
この TypeScript/Node.js 実装は、元の Python バージョンに比べて大きな利点を提供します。
簡素化されたインストール: 依存関係が少なくなり、セットアッププロセスが簡単になりました
クロスプラットフォーム互換性: 一般的なPythonの依存関係の問題なくWindows上でシームレスに動作します
より高速なパフォーマンス: Node.js は通常、このタイプのサーバーでより優れたパフォーマンスを提供します。
最新のJavaScriptエコシステム：堅牢なnpmパッケージエコシステムを活用
より簡単な統合: Webアプリケーションや他のJavaScriptツールとの統合が簡単
リソース使用量の低減: 一般的にメモリとシステムリソースの必要量が少なくなります
Python の依存関係、仮想環境、互換性の問題を気にせずに OSP マーケティング ツールを使用したい方に最適です。
Related MCP server: WithSeismic MCP
製品の価値とポジショニングを効果的に伝える、構造化されたOSP製品価値マップを作成します。このツールは以下の機能を提供します。
戦略的なタグラインの作成と改良
市場、技術、UX、ビジネスの各側面におけるポジションステートメント
役割、課題、ニーズを考慮したペルソナ開発
明確なメリット、課題、解決策を記載した価値事例ドキュメント
構造化された階層における特徴の分類
適切なキーワード配置とSEOに適した構造で、ウェブコンテンツに最適なメタデータを作成します。このツールは以下の機能を提供します。
戦略的なキーワード配置による記事タイトル（H1）
検索に最適化されたメタタイトル（50～60文字）
説得力のある価値提案を含むメタディスクリプション（155～160文字）
SEOに適したURLスラッグ
検索意図の分析と最適化
モバイルディスプレイの考慮事項
包括的なコンテンツレビューには、 OSPのセマンティック編集コードを適用します。このシステムには以下が含まれます。
スコープと物語構造の分析
フローと読みやすさの向上
スタイルとフレーズの最適化
単語の選択と文法の検証
技術的精度の検証
包括的な言語ガイダンス
前後の例を挙げた建設的なフィードバック
適切な構成と流れを備えた高品質な技術コンテンツを作成するための体系的なアプローチ。このガイドでは以下の内容を取り上げます。
物語構造の原則と論理的展開
フローの最適化とコンテンツの整理
明確さと正確さのためのスタイルガイドライン
技術的正確性の検証
コンテンツタイプ固有のガイダンス（チュートリアル、リファレンスドキュメント、APIドキュメント）
アクセシビリティと国際化のベストプラクティス
検索エンジンとユーザーエクスペリエンスのためにウェブコンテンツを最適化する包括的なシステム。このガイドには以下が含まれます。
メタコンテンツ最適化戦略
コンテンツの深みを高める技術
さまざまなクエリタイプ間での検索意図の調整
キーワード調査と統合プロトコル
内部リンクのベストプラクティス
構造化データの実装
コンテンツプロモーション戦略
Node.js 18以上
npmまたはyarn
このパッケージは、次の 2 つの方法でインストールして使用できます。
パッケージをグローバルにインストールします。
Claude Desktop 構成ファイル ( claude_desktop_config.json ) に以下を追加します。
{
"mcpServers": {
"ai_think_gate": {
"command": "npx",
"args": [
"-y",
"@trishchuk/osp-marketing-tools-mcp"
]
}
}
}# Clone the repository
git clone https://github.com/x51xxx/osp-marketing-tools-mcp.git
cd osp-marketing-tools-mcp
# Install dependencies
npm install
# Build the project
npm run buildClaude Desktop 構成ファイル ( claude_desktop_config.json ) に以下を追加します。
{
"mcpServers": {
"osp_marketing_tools": {
"command": "node",
"args": [
"path/to/osp-marketing-tools-mcp/dist/index.js"
]
}
}
}Windowsユーザーへの注意： path/to/osp-marketing-tools-mcpお使いのコンピュータ上のosp-marketing-tools-mcpディレクトリへの実際のパスに置き換えてください。例えば、リポジトリをC:\Users\YourName\Documents\osp-marketing-tools-mcpにクローンした場合、 args行は次のようになります。
"args": ["C:\\Users\\YourName\\Documents\\osp-marketing-tools-mcp\\dist\\index.js"]また、パスでは単一のバックスラッシュ\ではなく二重のバックスラッシュ\\使用することを忘れないでください。
npm startnpm run start:sseこれにより、Server-Sent Events (SSE) を介して MCP API を公開するポート 3000 (PORT 環境変数で構成可能) で Web サーバーが起動します。
サーバーが HTTP/SSE モードで実行されると、利用可能なツールのインタラクティブなデモを提供するhttp://localhost:3000/の Web インターフェイスにアクセスできるようになります。
health_check - サーバーの健全性状態とリソースの可用性のチェック
get_editing_codes - セマンティックコンテンツレビューのための OSP 編集コードのドキュメントと使用プロトコル
get_writing_guide - 高品質な技術コンテンツを作成するための原則をまとめた OSP ライティングガイド
get_meta_guide - Webコンテンツのメタデータ最適化のためのOSPメタ情報ジェネレーター
get_value_map_positioning_guide - 効果的な製品ポジショニングのための OSP 製品バリューマップ ジェネレーター
get_on_page_seo_guide - 包括的なコンテンツ最適化のための OSP オンページ SEO ガイド
edit-content - OSP編集コードを使用してコンテンツをレビューし、品質と明瞭性を向上させます
generate-meta - 適切なキーワード配置でウェブコンテンツに最適化されたメタデータを生成する
generate-value-map - 製品ポジショニングのための構造化されたOSPバリューマップを作成する
apply-writing-guide - OSP ライティング原則を適用して技術コンテンツを作成する
optimize-seo - オンページSEO戦略を適用して、検索用にコンテンツを最適化します
OSP 編集コードを使用してコンテンツを確認するには、次のプロンプトを Claude に送信するだけです (MCP サーバーを設定した後)。
Review this technical content using OSP editing codes:
Kubernetes is a container orchestration platform. It handles deploying applications and scaling them as needed. There's lots of things it can do. It's really good. You should use it for your applications to make them more resilient.製品価値マップを生成するには:
Generate an OSP value map for [Product Name] focusing on [target audience] with the following key features: [list features]
Example:
Generate an OSP value map for CloudDeploy focusing on DevOps engineers with these key features:
- Automated deployment pipeline
- Infrastructure as code support
- Real-time monitoring
- Multi-cloud compatibilityサーバーは、Server-Sent Events（SSE）を使用してMCPサーバーに接続する方法を示すJavaScriptクライアントサンプルを提供します。サーバーをHTTP/SSEモードで実行すると、 http://localhost:3000/でデモにアクセスできます。
以下は、プログラムで SSE エンドポイントに接続する方法の簡単な例です。
// Connect to SSE endpoint
const source = new EventSource('http://localhost:3000/sse');
let sessionId;
// Listen for session ID
source.addEventListener('sessionId', (event) => {
sessionId = JSON.parse(event.data).sessionId;
console.log(`Connected with session ID: ${sessionId}`);
});
// Listen for tool responses
source.addEventListener('toolResponse', (event) => {
const response = JSON.parse(event.data);
console.log('Tool response:', response);
});
// Call a tool
async function callTool(name, args = {}) {
const response = await fetch(`http://localhost:3000/messages?sessionId=${sessionId}`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
jsonrpc: '2.0',
id: Date.now().toString(),
method: 'tools/call',
params: {
name: name,
arguments: args
}
})
});
return response.json();
}# Run in development mode (stdio)
npm run dev
# Run in development mode (HTTP/SSE)
npm run dev:sseosp-marketing-tools-mcp/
├── prompts/ # Markdown resources from OSP tools
├── public/ # Static web files for HTTP server
│ └── index.html # SSE client demo interface
├── src/
│ ├── utils/ # Utility functions
│ │ └── contentReader.ts
│ ├── tools/ # MCP tool implementations
│ │ └── index.ts
│ ├── prompts/ # MCP prompt templates
│ │ └── index.ts
│ ├── resources/ # MCP resource implementations
│ │ └── index.ts
│ ├── index.ts # Main entry point (stdio)
│ └── http.ts # HTTP/SSE server
├── dist/ # Compiled JavaScript (generated)
├── package.json
└── tsconfig.jsonこのソフトウェアは、 Open Strategy Partnersが開発したコンテンツ作成および最適化手法に基づいています。LLM対応のマーケティングツールとプロフェッショナルなコンテンツ作成フレームワークを実装しています。
詳細情報と元のリソースについては、次の Web サイトをご覧ください。
このソフトウェアは、Creative Commons Corporation (以下「Creative Commons」) の Attribution-ShareAlike 4.0 International ライセンスに基づいてライセンスされています。
Get the Open Strategy Partners (OSP) editing codes documentation and usage protocol for editing texts. These semantic editing marks provide a standardized framework for content review with a teaching/learning focus.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 describes what the tool returns (documentation and usage protocol) but doesn't cover important aspects like whether it's a read-only operation, potential rate limits, authentication requirements, or error handling. For a tool with zero annotation coverage, this leaves significant gaps in understanding its 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?
The description is appropriately concise with two sentences that efficiently convey the tool's purpose and the nature of the content. It's front-loaded with the main action and resource, though it could be slightly more structured by explicitly separating purpose from content characteristics.
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 has no parameters and no output schema, the description provides adequate basic information about what the tool does. However, it lacks details about the return format (e.g., whether it's structured data, a document, or a reference), which would be helpful since there's no output schema. For a simple retrieval tool, this is minimally viable 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?
The tool has zero parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, which is efficient and correct for this case.
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: retrieving OSP editing codes documentation and usage protocol for editing texts. It specifies the resource (OSP editing codes) and the action (get), though it doesn't explicitly differentiate from sibling tools like 'get_writing_guide' or 'get_meta_guide' beyond mentioning the specific content type.
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 no guidance on when to use this tool versus alternatives. It mentions the tool's content focus (semantic editing marks for content review with teaching/learning focus) but doesn't indicate when to choose it over sibling tools like 'get_writing_guide' or 'get_on_page_seo_guide', nor does it specify any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the Open Strategy Partners (OSP) Web Content Meta Information Generation System for creating optimized article titles, meta titles, meta descriptions, and slugs for web content with proper keyword placement and search intent analysis.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 describes what the system does (generates optimized content elements) but lacks details on behavioral traits such as whether this is a read-only operation, if it requires authentication, rate limits, or what the output format looks like. This is a significant gap for a tool with zero annotation coverage.
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 run-on sentence that packs multiple concepts (system name, purpose, elements generated, and analysis features). While it conveys key information, it could be more structured and front-loaded for clarity. It's not overly verbose but could be tightened for better readability.
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 implied by generating optimized content with search intent analysis, and with no annotations or output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a guide, template, or generated text), how to interpret results, or any operational constraints, leaving gaps for effective 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?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, which is efficient. However, it could have mentioned if any implicit inputs or context are required, but this is minor, warranting a high score.
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 the tool retrieves a specific system (OSP Web Content Meta Information Generation System) for creating optimized web content elements, which is a clear purpose. However, it doesn't distinguish this from sibling tools like 'get_on_page_seo_guide' or 'get_writing_guide' that might cover overlapping SEO/content optimization areas, making it somewhat vague about its unique scope.
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 generating article titles, meta titles, descriptions, and slugs with keyword placement and search intent analysis, but provides no explicit guidance on when to use this tool versus alternatives like 'get_on_page_seo_guide' or 'get_writing_guide'. There are no exclusions or prerequisites mentioned, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the Open Strategy Partners (OSP) On-Page SEO Optimization Guide for comprehensive web content optimization, covering meta content, keyword research, content depth, search intent alignment, internal linking, and structured data.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 states this is a 'Get' operation which implies read-only behavior, but doesn't explicitly confirm this or mention any other behavioral traits like authentication requirements, rate limits, response format, or potential side effects. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
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, well-structured sentence that efficiently conveys the tool's purpose and scope. It front-loads the core function ('Get the Open Strategy Partners (OSP) On-Page SEO Optimization Guide') and then provides useful detail about what the guide covers. Every part of the sentence adds value without 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?
For a zero-parameter tool with no output schema and no annotations, the description provides adequate but minimal information. It tells what resource is retrieved and what topics it covers, but doesn't describe the return format, potential errors, or any operational constraints. Given the simplicity of the tool (no parameters), the description meets minimum viable standards but could be more complete regarding behavioral expectations.
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 tool has zero parameters (input schema is empty object), so there are no parameters to document. The description appropriately doesn't discuss parameters since none exist. With 100% schema description coverage and zero parameters, a baseline of 4 is appropriate as the description doesn't need to compensate for any parameter documentation gaps.
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: 'Get the Open Strategy Partners (OSP) On-Page SEO Optimization Guide' with specific details about what the guide covers (meta content, keyword research, etc.). It uses a specific verb ('Get') and identifies the resource (OSP guide). However, it doesn't explicitly differentiate from sibling tools like 'get_meta_guide' or 'get_writing_guide' beyond mentioning SEO focus.
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 no guidance on when to use this tool versus alternatives. It doesn't mention when this SEO guide would be preferred over the 'get_meta_guide' or 'get_writing_guide' siblings, nor does it specify any prerequisites or contextual triggers for its use. The description simply states what the tool provides without usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the Open Strategy Partners (OSP) Product Communications Value Map Generation System for Product Positioning, including taglines, position statements, personas, value cases, and feature categorization in a structured hierarchy.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 implies a read-only operation ('Get') but doesn't specify whether it requires authentication, has rate limits, returns structured vs. unstructured data, or handles errors. For a tool with zero annotation coverage, this leaves significant gaps in understanding its 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?
The description is a single, well-structured sentence that efficiently lists the tool's purpose and key components without redundancy. It is appropriately sized for a no-parameter tool, though it could be slightly more front-loaded by starting with the core action ('Get the OSP Product Communications Value Map Generation System').
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 (retrieving a structured system with multiple components), no annotations, and no output schema, the description is moderately complete. It specifies what content is included but lacks details on return format, data structure, or behavioral traits, which are needed for full contextual understanding.
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 tool has zero parameters, and schema description coverage is 100% (as there are no parameters to describe). The description doesn't need to add parameter semantics, so a baseline score of 4 is appropriate, as it efficiently avoids unnecessary detail while matching the schema's completeness.
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: retrieving a specific system (OSP Product Communications Value Map Generation System) with detailed content components (taglines, position statements, personas, value cases, feature categorization). It uses a specific verb ('Get') and identifies the resource, though it doesn't explicitly differentiate from sibling tools like 'get_meta_guide' or 'get_writing_guide' beyond naming the specific system.
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 is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, appropriate contexts, or exclusions, nor does it reference sibling tools like 'get_meta_guide' or 'get_writing_guide' to help the agent choose between them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the Open Strategy Partners (OSP) writing guide and usage protocol for creating high-quality technical content. This guide provides systematic principles for narrative structure, flow, style, and technical accuracy.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 describes what the tool returns ('writing guide and usage protocol'), but does not disclose any behavioral traits such as authentication requirements, rate limits, response format, or potential side effects. For a tool with zero annotation coverage, this is a significant gap in transparency.
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. The first sentence clearly states the tool's purpose, and the second sentence adds useful context about the guide's content. Every sentence earns its place with no redundant or vague language, making it efficient and well-structured.
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 (simple retrieval with no parameters) and the lack of annotations and output schema, the description is minimally adequate. It explains what the tool does but does not provide enough context for full understanding, such as response format or behavioral details. Without an output schema, the description should ideally hint at return values, but it does not, leaving some gaps.
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 tool has 0 parameters, and the input schema has 100% description coverage (though empty). With no parameters, the baseline score is 4, as there is nothing for the description to compensate for. The description does not need to add parameter semantics, so it meets expectations without extra effort.
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: 'Get the Open Strategy Partners (OSP) writing guide and usage protocol for creating high-quality technical content.' It specifies the verb ('Get') and resource ('writing guide and usage protocol'), and mentions the content's scope ('systematic principles for narrative structure, flow, style, and technical accuracy'). However, it does not explicitly differentiate this tool from its siblings (e.g., get_editing_codes, get_meta_guide), which would be needed for a score of 5.
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 no guidance on when to use this tool versus its siblings. It mentions the guide's purpose ('for creating high-quality technical content'), but does not specify scenarios, prerequisites, or alternatives. For example, it does not clarify when to choose this over get_editing_codes or get_meta_guide, leaving usage context implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check if the server is running and can access its resources
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions checking server status and resource accessibility, but doesn't specify what constitutes 'running' or 'resources', whether this is a lightweight ping or intensive diagnostic, what authentication might be needed, or what the output format looks like. For a tool with zero annotation coverage, this leaves significant behavioral 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 a single, efficient sentence that communicates the core purpose without any wasted words. It's appropriately sized for a simple health check tool and front-loads the essential information ('Check if the server is running').
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 simplicity (no parameters, no output schema, no annotations), the description provides adequate basic context about what the tool does. However, it lacks details about behavioral aspects (what 'check' entails, response format, error conditions) that would be helpful for an agent to understand the tool's operation fully, especially with no annotations to supplement.
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 tool has zero parameters, and schema description coverage is 100% (empty schema is fully described as having no parameters). The description appropriately doesn't discuss parameters since none exist, which meets expectations for this dimension. A perfect score would require the description to explicitly note the lack of parameters, but it's reasonable to assume this from context.
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 with specific verbs ('check if...is running and can access') and resources ('server', 'its resources'), making it immediately understandable. However, it doesn't distinguish this health check tool from its siblings (all content/guide retrieval tools), which would require explicit differentiation for a perfect score.
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 no guidance on when to use this tool versus alternatives or in what context it should be invoked. There are no prerequisites mentioned, no exclusions, and no reference to sibling tools, leaving the agent with no usage framework beyond the basic purpose statement.
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.
get_editing_codesget_meta_guideget_on_page_seo_guideget_value_map_positioning_guideget_writing_guidehealth_checkScored across 6 tools
Each tool has a clearly distinct purpose with no overlap: editing codes, meta guide, SEO guide, value map positioning, writing guide, and health check. The descriptions specify unique domains (e.g., semantic editing marks vs. web content optimization), making misselection unlikely.
All tools follow a consistent verb_noun pattern with 'get_' prefix for five tools and a descriptive 'health_check' for the sixth. The naming is uniform and predictable, using snake_case throughout without deviations.
Six tools are reasonable for a marketing-focused server, covering key areas like SEO, content creation, and positioning. It's slightly thin but well-scoped; each tool earns its place without redundancy or obvious gaps in the core domain.
The tools provide comprehensive guides for content creation, SEO, and positioning, but they are all 'get' operations with no create/update/delete actions. This limits agent workflows to retrieval only, leaving notable gaps for interactive or generative tasks in the marketing domain.
A Model Context Protocol server for Wix AI tools
Marketo MCP server for AI. 130 tools to operate Marketo from Claude, Cursor, or ChatGPT.
The Mercado Pago MCP Server implements the Model Context Protocol to provide AI agents and LLMs with access to Mercado Pago's APIs and tools within compatible development environments. It acts as an intermediary that translates Mercado Pago resources into executable functions (tools) that AI applications can invoke to perform actions and automate flows. The server simplifies integration, enables using documentation to implement or improve code, and optimizes operations through natural language interactions without manual implementations.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
