Which integrations are available for this server?

Offers JavaScript-specific prompts from promptz.dev, including content related to JavaScript development that can be accessed and used directly without copy-pasting. Integrates with npm through the npx command for easy installation and usage of the MCP server without requiring local installation. Provides access to React-focused prompts from promptz.dev, such as 'React Component Generator' for creating React components and 'React Component Documentation' for improving documentation.

How do I use promptz.dev MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@promptz.dev MCP Server search for React prompts about state management" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

en es ja ko zh

promptz.dev MCP Server

by cremich

Overview Schema Related Servers Score Discussions

TypeScript

Hybrid

promptz.dev MCP Server

Access prompts from promptz.dev directly within Amazon Q Developer.

This MCP server allows to access prompts from the promptz.dev API without copy-pasting, reducing context switching and friction in your development workflow.

Features

The promptz.dev MCP Server provides two main capabilities:

Prompts - Executable functions to search and execute prompts.
Rules - Executable functions to search for project rules and by integrating with other tools adding/updating them in your workspace.

Related MCP server: prompts.chat MCP Server

Example Usage

Once the server is connected to Amazon Q Developer, you can use it with natural language like:

"Search for CLI prompts about JavaScript"
"Show me the prompt called 'React Component Documentation'"
"Use the React Component Documentation prompt to improve my documentation"
"Find project rules for CDK Development"
"Add the CDK Project Structure project rule to my workspace"

Installation

Step 1: Get API Credentials

Navigate to https://promptz.dev/mcp
Copy the MCP settings like API Key, API URL or the sample MCP configuration snippet.

Step 2: Install the MCP Server

Open the Amazon Q Developer MCP client settings file located at ~/.aws/amazonq/mcp.json

Option 1: Using npx (Recommended)

The easiest way to use the server is with npx, which doesn't require installation:

Add the following configuration to your Amazon Q Developer MCP client's settings file:

{
  "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": []
    }
  }
}

Option 2: Local Installation

Clone the repository:

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

Install dependencies and build:

npm install
npm run build

Add the following configuration to your MCP client's settings file:

{
  "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": []
    }
  }
}

Troubleshooting

If you encounter issues with the server:

Check that your API credentials are correct
Ensure the server is properly configured in your MCP client
Look for error messages in the logs located ad ~/.promptz/logs/mcp-server.log
Use the MCP Inspector for debugging:

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

The Inspector will provide a URL to access debugging tools in your browser.

Development

For those who want to contribute or modify the server:

# Install dependencies
npm install

# Build the server
npm run build

# For development with auto-rebuild
npm run watch

# Run tests
npm test

Security Considerations

This server only provides read access to prompts and does not implement any write operations
API credentials are stored in your MCP client's configuration file
All communication with the promptz.dev API is done via HTTPS
The server logs to a file in your home directory (~/.promptz/logs/mcp-server.log)

Available Tools

4 tools

get_promptC

Get a specific prompt by ID or name

ParametersJSON Schema

Name	Required	Description	Default
`name`	No	Name 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

Name	Required	Description	Default
`name`	No	Name 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

Name	Required	Description	Default
`cursor`	No	Pagination token for fetching the next set of results
`tags`	No	Filter 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

Name	Required	Description	Default
`cursor`	No	Pagination token for fetching the next set of results
`tags`	No	Filter 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.

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

B3.4/5.0

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

ResponsivenessSyncing

Resources

Need Help?

Related Servers

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Ultra Prompt
co.ultraprompt
Your prompt library inside your AI: 1,000+ pro templates, frameworks, vocab & pipelines.
AI & Machine Learning Developer Tools Documentation Access
2
.prompts AOAuth
com.dotprompts
.prompts, the home to all your AI prompts, everywhere you need them.
Note Taking Developer Tools Knowledge & Memory
2
PromptOT
com.promptot
Manage, version, and publish LLM prompts with blocks, variables, and evaluations.
Developer Tools AI & Machine Learning RAG Systems
2
ПромтЛаб — библиотека AI-промптов
ru.promtlabs
Self-hosted AI prompt library: prompts, collections, tags, teams, chains. 29 MCP tools for agents.
AI & Machine Learning Knowledge & Memory Developer Tools
2

Related MCP Servers

Latitude MCP Server
Developer Tools Content Management Systems App Automation
yigitkonur
F
license
Not graded
quality
Not graded
maintenance
Enables AI-native prompt management for Latitude.so with full CRUD operations, version control, file-based workflows, and direct prompt execution with streaming support.
Updated 6 months ago (2026-02-23 00:48 UTC)
4
prompts.chat MCP Server
Search Developer Tools
f
A
license
B
quality
D
maintenance
Provides access to thousands of AI prompts from prompts.chat, enabling users to search, browse, and use community-curated prompts with variable substitution directly in their AI coding assistant.
Updated 5 months ago (2026-03-28 21:22 UTC)
2
178
33
ISC
MCP Prompt Manager
File Systems Developer Tools Note Taking
Tae4an
A
license
B
quality
D
maintenance
Enables AI models like Claude to manage local prompt files with CRUD operations, fuzzy search, categorization, templates, version control, and favorites.
Updated 9 months ago (2025-12-15 08:21 UTC)
32
5
MIT
Haiven MCP Server
Developer Tools Documentation Access
tw-haiven
A
license
Not graded
quality
F
maintenance
Connects AI tools to your organization's Haiven prompts, allowing seamless access to expert-crafted prompts for user stories, code reviews, and more without switching apps.
Updated 9 months ago (2025-11-24 14:01 UTC)
Apache 2.0

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/cremich/promptz-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server