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promptz.dev MCP Server

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promptz.dev MCP 서버

Amazon Q Developer에서 promptz.dev의 프롬프트에 직접 액세스하세요.

이 MCP 서버를 사용하면 복사-붙여넣기 없이 promptz.dev API에서 프롬프트에 액세스할 수 있으므로 개발 워크플로에서 컨텍스트 전환과 마찰이 줄어듭니다.

특징

promptz.dev MCP 서버는 두 가지 주요 기능을 제공합니다.

  1. 프롬프트 - 프롬프트를 검색하고 실행하는 실행 가능한 함수입니다.

  2. 규칙 - 프로젝트 규칙을 검색하고 다른 도구와 통합하여 작업 공간에 규칙을 추가/업데이트할 수 있는 실행 기능입니다.

Related MCP server: prompts.chat MCP Server

사용 예

서버가 Amazon Q Developer에 연결되면 다음과 같은 자연어로 사용할 수 있습니다.

  • "JavaScript에 대한 CLI 프롬프트 검색"

  • "React 컴포넌트 문서"라는 프롬프트를 보여주세요.

  • "React Component Documentation 프롬프트를 사용하여 내 문서를 개선하세요"

  • "CDK 개발을 위한 프로젝트 규칙 찾기"

  • "내 작업 공간에 CDK 프로젝트 구조 프로젝트 규칙 추가"

설치

1단계: API 자격 증명 가져오기

  1. https://promptz.dev/mcp 로 이동합니다.

  2. API 키, API URL 또는 샘플 MCP 구성 스니펫과 같은 MCP 설정을 복사합니다.

2단계: MCP 서버 설치

~/.aws/amazonq/mcp.json 에 있는 Amazon Q Developer MCP 클라이언트 설정 파일을 엽니다.

옵션 1: npx 사용(권장)

서버를 사용하는 가장 쉬운 방법은 설치가 필요하지 않은 npx를 사용하는 것입니다.

  1. Amazon Q Developer MCP 클라이언트의 설정 파일에 다음 구성을 추가합니다.

지엑스피1

옵션 2: 로컬 설치

  1. 저장소를 복제합니다.

git clone https://github.com/cremich/promptz-mcp.git
cd promptz-mcp

  1. 종속성을 설치하고 빌드합니다.

npm install
npm run build

  1. MCP 클라이언트의 설정 파일에 다음 구성을 추가합니다.

{
  "mcpServers": {
    "promptz.dev": {
      "command": "node",
      "args": ["/path/to/promptz-mcp/build/index.js"],
      "env": {
        "PROMPTZ_API_URL": "your-api-url-from-promptz.dev",
        "PROMPTZ_API_KEY": "your-api-key-from-promptz.dev"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

문제 해결

서버에 문제가 발생하는 경우:

  1. API 자격 증명이 올바른지 확인하세요

  2. MCP 클라이언트에서 서버가 올바르게 구성되었는지 확인하세요.

  3. ad ~/.promptz/logs/mcp-server.log 에 있는 로그에서 오류 메시지를 찾아보세요.

  4. 디버깅을 위해 MCP Inspector를 사용하세요.

# Run with environment variables
PROMPTZ_API_URL="your-api-url" PROMPTZ_API_KEY="your-api-key" npm run inspector

검사기는 브라우저에서 디버깅 도구에 액세스할 수 있는 URL을 제공합니다.

개발

서버에 기여하거나 수정하고 싶은 분들을 위해:

# Install dependencies
npm install

# Build the server
npm run build

# For development with auto-rebuild
npm run watch

# Run tests
npm test

보안 고려 사항

  • 이 서버는 프롬프트에 대한 읽기 액세스만 제공하며 쓰기 작업은 구현하지 않습니다.

  • API 자격 증명은 MCP 클라이언트의 구성 파일에 저장됩니다.

  • promptz.dev API와의 모든 통신은 HTTPS를 통해 이루어집니다.

  • 서버는 홈 디렉토리의 파일에 기록합니다(~/.promptz/logs/mcp-server.log)

Available Tools

4 tools
get_promptC

Get a specific prompt by ID or name

ParametersJSON Schema
NameRequiredDescriptionDefault
nameNoName of the prompt to retrieve

TDQS

C2.9/5.0
Behavior2/5

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 disclosure. It states the tool retrieves a prompt but doesn't describe what happens if the prompt doesn't exist (e.g., error handling), authentication needs, rate limits, or the format of the returned prompt. For a retrieval 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it easy to parse. Every word earns its place, achieving optimal conciseness for the tool's purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (1 parameter, 100% schema coverage) but lack of annotations and output schema, the description is incomplete. It doesn't explain the return value (e.g., prompt content or metadata), error conditions, or behavioral nuances. For a retrieval tool, this leaves the agent without key operational context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with the single parameter 'name' documented as 'Name of the prompt to retrieve'. The description adds that retrieval can be by 'ID or name', implying an alternative identifier not in the schema, but doesn't clarify how to specify an ID versus a name or if both are supported. This adds marginal value beyond the schema, meeting the baseline for high coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Get') and resource ('a specific prompt'), specifying retrieval by ID or name. It distinguishes from 'list_prompts' (which likely lists multiple prompts) but doesn't explicitly differentiate from 'get_rule' or 'list_rules', which operate on different resource types. The purpose is clear but sibling differentiation is incomplete.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 when to choose 'get_prompt' over 'list_prompts' (e.g., for detailed vs. summary views) or how it relates to 'get_rule' and 'list_rules'. Usage context is implied by the name but not explicitly stated, leaving gaps for the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_ruleC

Get a specific project rule by name

ParametersJSON Schema
NameRequiredDescriptionDefault
nameNoName of the rule to retrieve

TDQS

C2.9/5.0
Behavior2/5

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 the action ('Get') but doesn't describe whether this is a read-only operation, what happens if the rule doesn't exist, permissions required, or response format. This leaves significant gaps for a tool with no 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no wasted words, clearly front-loading the core action. It's appropriately sized for a simple tool with one parameter.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and a simple input schema, the description is incomplete. It lacks information on behavioral aspects like error handling, return values, or usage context, which are crucial for effective tool invocation by an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with the single parameter 'name' documented in the schema as 'Name of the rule to retrieve'. The description adds no additional parameter details beyond this, so it meets the baseline for high schema coverage without compensating value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('a specific project rule by name'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_rules' (which likely lists multiple rules vs. retrieving one specific rule).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 like 'list_rules' or other siblings. The description implies usage for retrieving a specific rule by name but doesn't clarify prerequisites, error conditions, or comparative contexts.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_promptsC

List available prompts from promptz.dev

ParametersJSON Schema
NameRequiredDescriptionDefault
cursorNoPagination token for fetching the next set of results
tagsNoFilter prompts by tags (e.g. ['CLI', 'JavaScript'])

TDQS

C2.9/5.0
Behavior2/5

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 but only states the basic action. It doesn't mention whether this is a read-only operation, how results are returned (e.g., pagination behavior implied by the 'cursor' parameter), rate limits, authentication needs, or what 'available prompts' means in context. This leaves significant gaps for a listing tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It's appropriately sized for a simple listing tool and front-loads the essential information, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description is incomplete for a tool with two parameters and implied pagination behavior. It doesn't explain what 'available prompts' includes (e.g., public vs. private), how results are structured, or error conditions, leaving the agent with insufficient context for reliable use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with both parameters ('cursor' and 'tags') clearly documented in the schema. The description adds no additional parameter information beyond what the schema provides, so it meets the baseline of 3 where the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('List') and resource ('available prompts from promptz.dev'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its sibling 'get_prompt' (which likely retrieves a single prompt) or 'list_rules' (which likely lists rules rather than prompts), missing the opportunity for full sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 like 'get_prompt' or 'list_rules'. There's no mention of use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_rulesB

List available project rules from promptz.dev

ParametersJSON Schema
NameRequiredDescriptionDefault
cursorNoPagination token for fetching the next set of results
tagsNoFilter rules by tags (e.g. ['CDK', 'React'])

TDQS

B3.1/5.0
Behavior2/5

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 but only states what the tool does without mentioning safety, permissions, rate limits, or response format. It lacks details on whether this is a read-only operation, what happens on errors, or how results are structured.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (list operation with filtering and pagination), no annotations, and no output schema, the description is minimally adequate but incomplete. It covers the basic purpose but lacks behavioral context and output details that would help an agent use it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, so parameters 'cursor' and 'tags' are well-documented in the schema itself. The description adds no additional parameter semantics, but the high schema coverage justifies the baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('List') and resource ('available project rules from promptz.dev'), making the purpose understandable. However, it doesn't differentiate this tool from its sibling 'list_prompts' or 'get_rule', which would be needed 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.

Usage Guidelines2/5

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 like 'get_rule' or 'list_prompts'. There's no mention of prerequisites, context, or exclusions, leaving the agent with insufficient usage direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 4 tool updates
    • First observedget_prompt
    • First observedget_rule
    • First observedlist_prompts
    • First observedlist_rules
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.4/5.0

Scored across 4 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: get_prompt retrieves a specific prompt, get_rule retrieves a specific rule, list_prompts lists all prompts, and list_rules lists all rules. There is no overlap or ambiguity between these four operations.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with snake_case (get_prompt, get_rule, list_prompts, list_rules). The naming is perfectly predictable and readable throughout the set.

Tool Count4/5

With 4 tools, the count is reasonable and well-scoped for a prompt management server, covering core retrieval and listing operations. It's slightly lean but appropriate for the apparent scope, lacking only minor gaps like update or delete operations.

Completeness3/5

The tool set provides good read/list coverage for prompts and rules, but there are notable gaps in CRUD operations. For example, there are no tools for creating, updating, or deleting prompts or rules, which limits full lifecycle management for the domain.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Related MCP Connectors

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