PromptArchitect MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| LOG_LEVEL | No | Logging level: debug, info, warn, error | info |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_promptA | Transform a raw idea into a well-structured, actionable prompt optimized for AI assistants. Use this tool when you need to: • Create a new prompt from scratch • Structure a vague idea into a clear request • Generate role-specific prompts (coding, writing, research, etc.) Supports templates: coding (for programming tasks), writing (for content creation), research (for investigation), analysis (for data/business analysis), factcheck (for verification), general (versatile). IMPORTANT: When available, pass workspace context (file structure, package.json, tech stack) to generate prompts that align with the user's project. |
| refine_promptA | Iteratively improve an existing prompt based on specific feedback. Use this tool when you need to: • Improve a prompt that didn't get good results • Add missing context or constraints • Make a prompt more specific or clearer • Adapt a prompt for a different AI model The tool preserves the original structure while applying targeted improvements. IMPORTANT: When available, pass workspace context (file structure, package.json, tech stack) to ensure refined prompts comply with the user's project scope and original request. |
| analyze_promptA | Evaluate prompt quality and get actionable improvement suggestions. Use this tool when you need to: • Assess if a prompt is well-structured • Identify weaknesses before using a prompt • Get specific suggestions for improvement • Compare prompt quality before/after refinement Returns scores (0-100) for: clarity, specificity, structure, actionability. |
| get_server_statusA | Get PromptArchitect server status and performance metrics. Use this tool to check: • Whether AI (Gemini) is available • Cache hit rate and request statistics • Average response latency |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| Template Categories | List of available template categories |
| Debug Code | Analyze and fix bugs in code |
| Code Review | Review code for quality, security, and best practices |
| Blog Post | Generate a blog post outline or draft |
| Professional Email | Draft a professional email |
| Research Summary | Summarize research findings |
| SWOT Analysis | Conduct a SWOT analysis |
| Comparison Analysis | Compare multiple options or solutions |
| Fact Check | Verify claims and statements |
| Coding Templates | All templates in the coding category |
| Writing Templates | All templates in the writing category |
| Research Templates | All templates in the research category |
| Analysis Templates | All templates in the analysis category |
| Factcheck Templates | All templates in the factcheck category |
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose with no overlap: analyze_prompt evaluates quality, generate_prompt creates new prompts, get_server_status checks system metrics, and refine_prompt iteratively improves existing prompts. The descriptions explicitly differentiate their use cases, making misselection unlikely.
All tool names follow a consistent verb_noun pattern using snake_case: analyze_prompt, generate_prompt, get_server_status, and refine_prompt. This predictable naming scheme enhances readability and agent usability.
With 4 tools, the server is well-scoped for prompt engineering tasks. Each tool earns its place by covering distinct aspects of the domain: analysis, generation, refinement, and system monitoring, without being overly sparse or bloated.
The tool set provides strong coverage for core prompt engineering workflows, including creation, evaluation, and refinement. A minor gap exists in lacking a tool for deleting or managing saved prompts, but agents can work around this, and the surface supports most common operations effectively.