Claude Code Prompt Engineer
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 |
|---|---|
| engineer_promptB | Intelligently engineers and optimizes prompts for Claude Code, with interactive refinement and automatic optimization for maximum effectiveness. |
| ask_clarificationC | Ask clarifying questions to better understand user requirements and refine the prompt engineering process. |
| answer_questionsC | Provide answers to clarifying questions and continue the prompt engineering process. |
| auto_optimizeB | Automatically detects and optimizes natural language text for Claude Code. Use this when the user is writing conversational text that should be translated into an optimized prompt. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
There is some overlap between tools, particularly 'auto_optimize' and 'engineer_prompt' both involving prompt optimization, which could cause confusion. However, 'answer_questions' and 'ask_clarification' are clearly distinct, and descriptions help differentiate the optimization tools by specifying different use cases (natural language text vs. general prompt engineering).
The tools follow a consistent verb_noun pattern throughout (e.g., answer_questions, ask_clarification, auto_optimize, engineer_prompt), with all using snake_case. There is a minor deviation in that 'auto_optimize' and 'engineer_prompt' are more descriptive phrases rather than simple verb_noun combos, but the overall naming is predictable and readable.
With 4 tools, this is a well-scoped set for a Claude Code prompt engineering server. Each tool appears to serve a distinct purpose in the workflow (clarifying, optimizing, engineering), and the count is neither too thin nor too heavy, fitting the domain appropriately.
The tool surface covers key aspects of prompt engineering: gathering requirements (ask_clarification), providing feedback (answer_questions), and optimization (auto_optimize and engineer_prompt). A minor gap might be the lack of a tool for testing or evaluating prompts, but the core workflow is well-covered, and agents can likely work around this.