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promptz.dev MCP Server

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TypeScript
Hybrid

Servidor MCP promptz.dev

Acceda a las indicaciones de promptz.dev directamente dentro de Amazon Q Developer.

Este servidor MCP permite acceder a las indicaciones de la API promptz.dev sin copiar y pegar, lo que reduce el cambio de contexto y la fricción en el flujo de trabajo de desarrollo.

Características

El servidor MCP promptz.dev proporciona dos capacidades principales:

  1. Avisos : funciones ejecutables para buscar y ejecutar avisos.

  2. Reglas : Funciones ejecutables para buscar reglas del proyecto y, al integrarlas con otras herramientas, agregarlas o actualizarlas en su espacio de trabajo.

Related MCP server: prompts.chat MCP Server

Ejemplo de uso

Una vez que el servidor esté conectado a Amazon Q Developer, puedes usarlo con lenguaje natural como:

  • Buscar indicaciones de la CLI sobre JavaScript

  • "Muéstrame el mensaje llamado 'Documentación del componente React'"

  • "Utilice la solicitud de documentación de componentes de React para mejorar mi documentación"

  • Encontrar reglas de proyecto para el desarrollo de CDK

  • "Agregar la regla de proyecto Estructura del proyecto CDK a mi espacio de trabajo"

Instalación

Paso 1: Obtener credenciales de API

  1. Vaya a https://promptz.dev/mcp

  2. Copie la configuración de MCP, como la clave API, la URL de API o el fragmento de configuración de MCP de muestra.

Paso 2: Instalar el servidor MCP

Abra el archivo de configuración del cliente MCP de Amazon Q Developer ubicado en ~/.aws/amazonq/mcp.json

Opción 1: Usar npx (recomendado)

La forma más sencilla de utilizar el servidor es con npx, que no requiere instalación:

  1. Agregue la siguiente configuración al archivo de configuración de su cliente MCP de Amazon Q Developer:

{
  "mcpServers": {
    "promptz.dev": {
      "command": "npx",
      "args": ["-y", "@promptz/mcp"],
      "env": {
        "PROMPTZ_API_URL": "your-api-url-from-promptz.dev",
        "PROMPTZ_API_KEY": "your-api-key-from-promptz.dev"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

Opción 2: Instalación local

  1. Clonar el repositorio:

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

  1. Instalar dependencias y compilar:

npm install
npm run build

  1. Agregue la siguiente configuración al archivo de configuración de su cliente MCP:

{
  "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": []
    }
  }
}

Solución de problemas

Si encuentra problemas con el servidor:

  1. Comprueba que tus credenciales de API sean correctas

  2. Asegúrese de que el servidor esté configurado correctamente en su cliente MCP

  3. Busque mensajes de error en los registros ubicados en ~/.promptz/logs/mcp-server.log

  4. Utilice el Inspector MCP para depurar:

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

El Inspector proporcionará una URL para acceder a las herramientas de depuración en su navegador.

Desarrollo

Para aquellos que quieran contribuir o modificar el servidor:

# Install dependencies
npm install

# Build the server
npm run build

# For development with auto-rebuild
npm run watch

# Run tests
npm test

Consideraciones de seguridad

  • Este servidor solo proporciona acceso de lectura a las indicaciones y no implementa ninguna operación de escritura.

  • Las credenciales de API se almacenan en el archivo de configuración de su cliente MCP

  • Toda la comunicación con la API promptz.dev se realiza a través de HTTPS

  • El servidor registra en un archivo en su directorio de inicio (~/.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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