Promptopia MCP
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Promptopia MCPshow me my marketing campaign prompts"
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.
Promptopia MCP
A powerful Model Context Protocol (MCP) server for managing, organizing, and reusing prompt templates with variable substitution and multi-message conversation structures.
Introduction
Promptopia MCP is a comprehensive MCP server that enables AI applications to efficiently manage prompt templates through the Model Context Protocol. The server provides persistent storage for both single-content prompts and sophisticated multi-message conversation templates, complete with automatic variable detection, substitution capabilities, and seamless integration with MCP-compatible AI clients.
Built with TypeScript and designed for scalability, Promptopia MCP transforms how you work with AI prompts by providing a centralized, reusable prompt management system that integrates directly into your AI workflow through the standardized MCP protocol.
Related MCP server: Clarifyprompt-MCP
Why Use This Server?
Centralized Prompt Management: Store, organize, and version all your prompt templates in one secure location with filesystem persistence, eliminating scattered prompt files and ensuring consistency across projects.
Advanced Multi-Message Support: Create sophisticated conversation templates with role-based message structures (user/assistant) that support both text and image content, enabling complex AI interaction patterns.
Intelligent Variable Substitution: Automatically detect variables using
{{variable}}syntax and apply dynamic values at runtime, making your prompts flexible and reusable across different contexts and use cases.Seamless MCP Integration: Native MCP protocol support means your prompts automatically become available as first-class resources in Claude Desktop, IDEs, and other MCP-compatible applications without additional configuration.
Future-Proof Architecture: Built with extensibility in mind, supporting prompt chains, conditional logic, versioning, and advanced workflow patterns for evolving AI interaction needs.
Features
Core Prompt Management
Create and Store Prompts: Add both single-content and multi-message prompts with automatic variable detection
Retrieve and List: Access individual prompts by ID or browse all available templates with metadata
Update and Delete: Modify existing prompts or remove outdated templates with full CRUD operations
Variable Application: Replace template variables with actual values for immediate use
Multi-Message Conversation Support
Role-Based Messages: Structure conversations with explicit user and assistant message roles
Mixed Content Types: Support for text and image content within the same conversation template
Cross-Message Variables: Extract and apply variables across multiple messages in a conversation
Format Conversion: Seamlessly convert between single-content and multi-message formats as needed
MCP Protocol Integration
Native MCP Prompts: Expose stored prompts as MCP prompts for direct use in compatible applications
Tool-Based Access: Complete set of MCP tools for prompt management operations
Standardized Communication: Full compliance with MCP protocol specifications for reliable integration
Real-Time Variable Resolution: Apply variables through the MCP protocol for dynamic prompt execution
Advanced Capabilities
Automatic Variable Detection: Parse
{{variable}}patterns from prompt content with intelligent extractionBackward Compatibility: Maintain support for existing single-content prompts while adding new features
File System Persistence: Reliable JSON-based storage with organized directory structure
Error Handling: Comprehensive error management with informative MCP-compliant error responses
Type Safety: Full TypeScript implementation with robust type definitions and validation
Planned Features
Conditional Logic: Add branching and conditional execution to prompt templates
Version Control: Track changes and maintain prompt history with rollback capabilities
Search and Tagging: Organize prompts with metadata and full-text search functionality
Import/Export: Backup and share prompt collections across different environments
Installation
Installing via Smithery
To install Promptopia for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @lumile/promptopia-mcp --client claudeOption 1: NPX (Recommended)
Add this configuration to your Claude Desktop config file:
{
"mcpServers": {
"promptopia-mcp": {
"command": "npx",
"args": [
"-y",
"promptopia-mcp"
],
"env": {
"PROMPTS_DIR": "<YOUR_PROMPTS_DIR>"
}
}
}
}Option 2: Local Installation
Clone the repository
Install dependencies:
npm installBuild the server:
npm run buildAdd this configuration to your Claude Desktop config:
{
"mcpServers": {
"promptopia-mcp": {
"command": "node",
"args": [
"/path/to/promptopia-mcp/build/index.js"
],
"env": {
"PROMPTS_DIR": "<YOUR_PROMPTS_DIR>"
}
}
}
}Debugging
Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:
npm run inspectorThe Inspector will provide a URL to access debugging tools in your browser.
Contributing
Contributions are extremely welcome! Please open a PR with new MCP servers or any other improvements to the codebase.
License
This project is licensed under the MIT License - see the LICENSE.md file for details.
Available Tools
7 toolsadd_multi_message_promptC
Adds a new multi-message prompt with role-based messages
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Name of the prompt | |
| description | No | Description of the prompt | |
| messages | Yes | Array of messages with roles |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states it 'adds a new' prompt, implying a creation/mutation operation, but doesn't disclose behavioral traits such as permissions needed, whether it's idempotent, error handling, or what happens on success (e.g., returns a prompt ID). This is a significant gap for a mutation 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, efficient sentence that front-loads the core action and key feature (role-based messages). There is zero waste, making it easy to parse and understand quickly.
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 mutation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a prompt ID or confirmation), error conditions, or interaction with sibling tools. Given the complexity of nested message structures and multiple siblings, more context is needed for effective 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?
Schema description coverage is 100%, so the schema fully documents parameters like 'name', 'description', and 'messages' with details on roles and content types. The description adds no additional meaning beyond implying role-based messages, which is already covered in the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
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 action ('adds') and resource ('multi-message prompt'), specifying it creates a new prompt with role-based messages. It distinguishes from simpler 'add_prompt' by highlighting the multi-message aspect, though it doesn't explicitly contrast with all siblings like 'apply_prompt' or 'update_prompt'.
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. With siblings like 'add_prompt', 'apply_prompt', and 'update_prompt', the description lacks context on use cases, prerequisites, or distinctions, leaving the agent to infer based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
add_promptC
Adds a new prompt to the system (single content format)
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Name of the prompt | |
| content | Yes | Content of the prompt with variables in {{variable}} format | |
| description | No | Description of the prompt |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states this is an 'Adds' operation (implying creation/mutation) but doesn't disclose behavioral traits like required permissions, whether prompts are unique, error conditions, or what happens on success. The mention of 'single content format' adds minimal context about input constraints.
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 front-loads the core action. It could be slightly more informative but avoids unnecessary words, making it appropriately concise for a basic tool definition.
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 no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns, error handling, or how it interacts with sibling tools. For a mutation tool in a system with multiple prompt-related tools, more context is needed to guide effective 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?
Schema description coverage is 100%, so parameters are well-documented in the schema. The description adds no additional meaning about parameters beyond implying 'single content format' relates to the 'content' field, but this is vague and doesn't enhance understanding beyond the schema's details on variable formatting.
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 verb ('Adds') and resource ('a new prompt'), specifying it's for a 'single content format'. This distinguishes it from 'add_multi_message_prompt' which likely handles multiple messages. However, it doesn't explicitly mention what distinguishes it from 'update_prompt' or other siblings beyond format.
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 like 'add_multi_message_prompt', 'update_prompt', or 'apply_prompt'. It mentions 'single content format' but doesn't explain when that format is appropriate or what the alternatives are.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
apply_promptC
Applies variables to a template prompt and returns the result
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ID of the prompt to apply | |
| variables | Yes | Object containing variable names and their values |
TDQS
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 states the tool applies variables and returns a result, but doesn't explain critical behaviors like whether this is a read-only operation, if it modifies data, authentication needs, error handling, or rate limits. For a tool with no annotations, 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, efficient sentence that directly states the tool's function without any wasted words. It's appropriately sized and front-loaded, making it easy to grasp quickly.
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 (2 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the 'result' looks like (e.g., text output, structured data), potential side effects, or how it relates to sibling tools. For a tool that likely involves prompt execution, more context is needed to guide effective 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 schema description coverage is 100%, so the schema already documents both parameters ('id' and 'variables') with descriptions. The description adds no additional meaning beyond what the schema provides, such as examples of variable usage or format details. Baseline 3 is appropriate when the schema does the heavy lifting.
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 action ('applies variables to a template prompt') and the outcome ('returns the result'), which is specific and understandable. However, it doesn't explicitly differentiate this tool from its siblings like 'add_prompt' or 'update_prompt', which would require mentioning it's for executing/rendering prompts rather than managing them.
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. With siblings like 'add_prompt', 'get_prompt', and 'update_prompt', it's unclear if this is for testing prompts, generating outputs, or another purpose. No context, exclusions, or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
delete_promptC
Deletes a prompt by its ID
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ID of the prompt to delete |
TDQS
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. While 'Deletes' implies a destructive mutation, the description doesn't specify whether this action is reversible, what permissions are required, whether it affects related data, or what happens on success/failure. For a destructive operation with zero annotation coverage, this is a significant gap.
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 extremely concise - a single sentence with zero wasted words. It's front-loaded with the core action and resource. Every word earns its place, making it easy to parse quickly.
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 destructive deletion tool with no annotations and no output schema, the description is inadequate. It doesn't explain what constitutes a valid prompt ID, whether deletion is permanent, what confirmation (if any) is required, what happens to prompt versions or dependencies, or what the tool returns. Given the complexity of a deletion operation and the lack of structured metadata, the description should provide more complete context.
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 schema description coverage is 100%, with the single parameter 'id' fully documented in the schema as 'ID of the prompt to delete'. The description adds no additional parameter information beyond what's already in the schema. According to the scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.
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 action ('Deletes') and the resource ('a prompt by its ID'), making the purpose immediately understandable. However, it doesn't differentiate this tool from other deletion operations that might exist in the system or explain what distinguishes deleting a prompt from other prompt operations like 'update_prompt' or 'apply_prompt'.
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. With sibling tools like 'update_prompt', 'get_prompt', and 'list_prompts', there's no indication of when deletion is appropriate versus modification or retrieval. No prerequisites, warnings, or alternative tools are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_promptB
Gets a prompt by its ID
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ID of the prompt to retrieve |
TDQS
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 tool 'Gets a prompt,' implying a read-only operation, but doesn't specify whether it requires authentication, what happens if the ID is invalid (e.g., returns an error or null), or any rate limits. This leaves significant gaps in understanding the tool's 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, clear sentence with zero waste: 'Gets a prompt by its ID.' It is appropriately sized and front-loaded, efficiently conveying the core purpose without unnecessary details.
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 low complexity (one parameter, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks context on usage, behavioral traits, or output, making it incomplete for effective agent use without additional inference or trial.
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 schema description coverage is 100%, with the single parameter 'id' fully documented in the schema as 'ID of the prompt to retrieve.' The description adds no additional meaning beyond this, such as format examples (e.g., UUID) or constraints, 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Gets') and resource ('a prompt') with a specific identifier ('by its ID'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its siblings like 'list_prompts' or 'apply_prompt', which would require mentioning this is for retrieving a single prompt by ID rather than listing multiple or applying one.
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 that this is for retrieving a single prompt by ID, as opposed to 'list_prompts' for multiple prompts or 'apply_prompt' for using a prompt, nor does it specify prerequisites like needing a valid prompt ID.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_promptsB
Lists all available prompts
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states the action without behavioral details. It doesn't disclose whether this is a read-only operation, how results are returned (e.g., pagination, sorting), or any constraints like rate limits or authentication needs, which is inadequate 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, efficient sentence with zero waste, front-loading the core action. It avoids redundancy and is appropriately sized for a simple list operation, making it easy for an agent to parse quickly.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'lists' entails (e.g., format, structure, or limitations of the returned prompts), leaving gaps in understanding the tool's behavior and results for a read operation.
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 doesn't add parameter details, which is appropriate, earning a baseline score of 4 for not introducing unnecessary information.
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 verb ('Lists') and resource ('all available prompts'), making the purpose immediately understandable. It distinguishes from siblings like 'get_prompt' (singular) and 'add_prompt' (creation), though it could be more specific about scope or format to reach 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 like 'get_prompt' (for a single prompt) or 'apply_prompt' (for execution). It lacks explicit context, prerequisites, or exclusions, leaving the agent to infer usage from tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_promptC
Updates an existing prompt (supports both single content and multi-message formats)
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ID of the prompt to update | |
| name | No | New name for the prompt | |
| description | No | New description for the prompt | |
| messages | No | New messages (converts single content to multi-message format) |
TDQS
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 tool updates prompts, implying mutation, but doesn't address critical aspects like required permissions, whether changes are reversible, error handling, or rate limits. The mention of format conversion adds some context, but overall, it lacks sufficient detail for a mutation 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, efficient sentence that front-loads the core action and key feature (format support). There's no wasted verbiage, though it could be slightly more structured (e.g., separating purpose from usage notes). It earns its place by conveying essential information concisely.
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 (mutation with 4 parameters, nested objects in 'messages', no output schema, and no annotations), the description is inadequate. It doesn't explain return values, error cases, or behavioral nuances like how partial updates are handled. For a tool that modifies data without structured safety hints, more completeness is needed to guide the agent effectively.
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?
Schema description coverage is 100%, so the schema fully documents all parameters (id, name, description, messages). The description adds minimal value beyond this, only implying that 'messages' can handle format conversion. No additional syntax, constraints, or examples are provided, making the baseline score appropriate.
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 action ('Updates') and resource ('an existing prompt'), specifying it handles both single content and multi-message formats. It distinguishes from siblings like 'add_prompt' (creation) and 'delete_prompt' (removal), though not explicitly named. However, it doesn't fully differentiate from 'add_multi_message_prompt' which might also handle multi-message formats, preventing 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 like 'add_prompt' for creation or 'apply_prompt' for usage. It mentions format support but doesn't specify prerequisites (e.g., needing an existing prompt ID) or exclusions (e.g., not for new prompts). This leaves the agent with minimal context for selection among siblings.
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.
7 tool updates
- First observed
add_multi_message_prompt - First observed
add_prompt - First observed
apply_prompt - First observed
delete_prompt - First observed
get_prompt - First observed
list_prompts - First observed
update_prompt
TDQS
Scored across 7 tools
Each tool has a clearly distinct purpose: add_multi_message_prompt and add_prompt handle different prompt creation formats, while apply_prompt, delete_prompt, get_prompt, list_prompts, and update_prompt cover specific CRUD operations. There is no overlap or ambiguity between tools.
All tools follow a consistent verb_noun pattern (e.g., add_prompt, delete_prompt, list_prompts) with clear, descriptive names. The naming is uniform throughout, making it easy to understand each tool's function at a glance.
With 7 tools, the server is well-scoped for managing prompts, covering creation (in two formats), retrieval, listing, updating, deletion, and application. This count is appropriate, providing comprehensive functionality without being overwhelming.
The tool set offers complete CRUD coverage for prompts, including both single-content and multi-message formats, along with an apply_prompt tool for variable substitution. There are no obvious gaps, enabling full lifecycle management of prompts.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
The prediction MCP — score your prompt before you generate, so you never waste a credit.
MCP server for Midjourney AI image generation and editing
Hugging Face Hub MCP — models, datasets, spaces
Related MCP Servers
- AlicenseBqualityBmaintenanceMCP for Replicate Flux Model. Generating images by prompts763107MIT
- AlicenseAqualityAmaintenanceAn MCP server that transforms vague prompts into platform-optimized prompts for 58 AI platforms across 7 categories. Send a raw prompt. Get back a version specifically optimized for Midjourney, DALL-E, Sora, Runway, ElevenLabs, Claude, ChatGPT, or any of the 58 supported platforms — with the right syntax, parameters, and structure each platform expects.2316012Apache 2.0
- AlicenseAqualityCmaintenanceMCP server providing on-demand access to official prompting guides for building robust meta-prompts.23MIT
- AlicenseAqualityDmaintenanceMCP server for community-driven prompt voting and discovery — submit, upvote, and find the best AI prompts.7MIT