Open Strategy Partners 의 검증된 방법론을 기반으로 기술 마케팅 콘텐츠 제작, 최적화 및 제품 포지셔닝을 위한 포괄적인 도구 모음입니다. OSP 마케팅 도구용 MCP 서버를 TypeScript로 구현한 것입니다.
이 TypeScript/Node.js 구현은 원래 Python 버전에 비해 상당한 이점을 제공합니다.
간소화된 설치 : 종속성 감소 및 더 간단한 설정 프로세스
크로스 플랫폼 호환성 : 일반적인 Python 종속성 문제 없이 Windows에서 원활하게 작동합니다.
더 빠른 성능 : Node.js는 일반적으로 이 유형의 서버에 대해 더 나은 성능을 제공합니다.
최신 JavaScript 생태계 : 강력한 npm 패키지 생태계 활용
더 쉬운 통합 : 웹 애플리케이션 및 기타 JavaScript 도구와의 통합이 더 간편해졌습니다.
낮은 리소스 사용량 : 일반적으로 메모리와 시스템 리소스가 덜 필요합니다.
Python 종속성, 가상 환경 또는 호환성 문제를 걱정하지 않고 OSP 마케팅 도구를 사용하고 싶은 사람들에게 적합합니다.
Related MCP server: WithSeismic MCP
제품의 가치와 포지셔닝을 효과적으로 전달하는 체계적인 OSP 제품 가치 맵을 생성하세요. 이 도구는 다음과 같은 기능을 제공합니다.
전략적 태그라인 생성 및 개선
시장, 기술, UX 및 비즈니스 차원에 걸친 입장 표명
역할, 과제, 필요 사항을 고려한 페르소나 개발
명확한 이점, 과제 및 솔루션을 포함하는 가치 사례 문서
구조화된 계층 구조의 기능 분류
적절한 키워드 배치와 SEO 친화적인 구조를 통해 웹 콘텐츠에 최적화된 메타데이터를 생성합니다. 이 도구는 다음과 같은 기능을 제공합니다.
전략적 키워드 배치를 통한 기사 제목(H1)
검색에 최적화된 메타 타이틀(50~60자)
매력적인 가치 제안을 담은 메타 설명(155~160자)
SEO 친화적인 URL 슬러그
검색 의도 분석 및 최적화
모바일 디스플레이 고려 사항
OSP의 의미론적 편집 코드를 적용하여 포괄적인 콘텐츠 검토를 수행합니다. 이 시스템은 다음을 제공합니다.
범위 및 서사 구조 분석
흐름과 가독성 향상
스타일 및 구문 최적화
단어 선택 및 문법 검증
기술적 정확도 검증
포괄적인 언어 지도
이전/이후 사례를 통한 건설적인 피드백
적절한 내러티브 구조와 흐름을 갖춘 고품질 기술 콘텐츠를 제작하는 체계적인 접근 방식입니다. 이 가이드는 다음 내용을 다룹니다.
서사 구조 원칙과 논리적 진행
흐름 최적화 및 콘텐츠 구성
명확성과 정확성을 위한 스타일 가이드라인
기술적 정확도 검증
콘텐츠 유형별 지침(튜토리얼, 참조 문서, API 문서)
접근성 및 국제화 모범 사례
검색 엔진 최적화 및 사용자 경험을 위한 웹 콘텐츠 최적화를 위한 종합 시스템입니다. 이 가이드에는 다음 내용이 포함됩니다.
메타 콘텐츠 최적화 전략
콘텐츠 심도 향상 기술
다양한 쿼리 유형에 따른 검색 의도 정렬
키워드 조사 및 통합 프로토콜
내부 링크 모범 사례
구조화된 데이터 구현
콘텐츠 홍보 전략
Node.js 18 이상
npm 또는 yarn
이 패키지는 두 가지 방법으로 설치하고 사용할 수 있습니다.
패키지를 전역으로 설치합니다.
Claude Desktop 구성 파일( claude_desktop_config.json )에 다음을 추가합니다.
지엑스피1
# 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 구성 파일( claude_desktop_config.json )에 다음을 추가합니다.
{
"mcpServers": {
"osp_marketing_tools": {
"command": "node",
"args": [
"path/to/osp-marketing-tools-mcp/dist/index.js"
]
}
}
}Windows 사용자 참고: path/to/osp-marketing-tools-mcp 컴퓨터의 osp-marketing-tools-mcp 디렉터리의 실제 경로로 바꿔야 합니다. 예를 들어, 저장소를 C:\Users\YourName\Documents\osp-marketing-tools-mcp 에 복제한 경우 args 줄은 다음과 같습니다.
"args": ["C:\\Users\\YourName\\Documents\\osp-marketing-tools-mcp\\dist\\index.js"]또한 경로에 단일 백슬래시 \\ \ 를 사용하는 것을 잊지 마세요.
npm startnpm run start:sse이렇게 하면 포트 3000(PORT 환경 변수를 통해 구성 가능)에서 웹 서버가 시작되고 SSE(Server-Sent Events)를 통해 MCP API가 노출됩니다.
서버가 HTTP/SSE 모드로 실행되면 http://localhost:3000/ 에서 웹 인터페이스에 액세스할 수 있으며, 여기서 사용 가능한 도구에 대한 대화형 데모를 볼 수 있습니다.
health_check - 서버 상태 및 리소스 가용성 확인
get_editing_codes - 의미적 콘텐츠 검토를 위한 OSP 편집 코드 문서 및 사용 프로토콜
get_writing_guide - 고품질 기술 콘텐츠 작성을 위한 원칙을 담은 OSP 작성 가이드
get_meta_guide - 웹 콘텐츠 메타데이터 최적화를 위한 OSP 메타 정보 생성기
get_value_map_positioning_guide - 효과적인 제품 포지셔닝을 위한 OSP 제품 가치 맵 생성기
get_on_page_seo_guide - 포괄적인 콘텐츠 최적화를 위한 OSP 온페이지 SEO 가이드
edit-content - OSP 편집 코드를 사용하여 콘텐츠를 검토하여 품질과 명확성을 향상시킵니다.
generate-meta - 적절한 키워드 배치를 통해 웹 콘텐츠에 최적화된 메타데이터를 생성합니다.
generate-value-map - 제품 포지셔닝을 위한 구조화된 OSP 가치 맵을 생성합니다.
apply-writing-guide - OSP 작성 원칙을 적용하여 기술 콘텐츠 작성
optimize-seo - 검색을 위한 콘텐츠 최적화를 위해 온페이지 SEO 전략 적용
OSP 편집 코드를 사용하여 콘텐츠를 검토하려면 MCP 서버를 구성한 후 다음 메시지를 Claude에게 보내면 됩니다.
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.제품 가치 맵을 생성하려면 다음을 수행하세요.
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 compatibility이 서버는 SSE(Server-Sent Events)를 사용하여 MCP 서버에 연결하는 방법을 보여주는 JavaScript 클라이언트 예제를 제공합니다. 서버를 HTTP/SSE 모드로 실행하면 http://localhost:3000/ 에서 데모에 액세스할 수 있습니다.
다음은 SSE 엔드포인트에 프로그래밍 방식으로 연결하는 방법에 대한 간단한 예입니다.
// 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.json이 소프트웨어는 Open Strategy Partners 에서 개발한 콘텐츠 제작 및 최적화 방법론을 기반으로 합니다. LLM 지원 마케팅 도구와 전문적인 콘텐츠 제작 프레임워크를 구현합니다.
더 많은 정보와 원본 자료를 보려면 다음 사이트를 방문하세요.
본 소프트웨어는 크리에이티브 커먼즈 코퍼레이션("크리에이티브 커먼즈")의 저작자표시-동일조건변경허락 4.0 국제 라이선스에 따라 라이선스가 부여되었습니다.
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…
