Skip to main content
Glama
AungMyoKyaw

BetterPrompt MCP

by AungMyoKyaw

betterprompt

Transforms user requests into optimized prompts using advanced prompt engineering techniques like chain-of-thought and role-based prompting.

Instructions

Transforms user requests into world-class, optimized prompts using advanced prompt engineering techniques.

This tool applies multiple optimization strategies including:

  • Chain-of-Thought reasoning for step-by-step thinking

  • Few-shot learning with relevant examples

  • Role-based prompting with expert personas

  • Context enhancement and output formatting

  • Self-consistency for complex problems

Perfect for enhancing any request, from simple questions to complex tasks, ensuring maximum AI performance and response quality.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainNoSpecific domain or field for specialized optimization (optional)
contextNoAdditional context or background information for the request (optional)
requestYesThe original user request to be optimized (required)
creativityNoLevel of creativity to encourage (default: high)high
constraintsNoSpecific constraints or requirements (optional)
desiredToneNoDesired tone for the response (optional)
outputFormatNoDesired format for the response (default: conversational)conversational
targetAudienceNoTarget audience for the response (optional)
includeExamplesNoWhether to include relevant examples in the optimized prompt (default: true)
optimizationLevelNoLevel of optimization to apply (default: advanced)advanced
enableChainOfThoughtNoEnable chain-of-thought reasoning instructions (default: true)
enableSelfConsistencyNoEnable multiple reasoning paths for complex problems (default: false)
enableContextEnhancementNoEnable context enrichment and structure enhancement (default: true)
enableRoleBasedPromptingNoEnable expert role assignment for enhanced authority (default: true)
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must fully convey behavioral traits. It describes the transformation and strategies but does not mention safety, side effects, or whether the tool is read-only. It lacks disclosure of important behavioral aspects like output details or limitations.

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 concise and front-loaded with the main purpose. The second paragraph lists techniques efficiently without redundancy. Every sentence contributes meaning, and the structure is clear.

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 complexity (14 parameters, no output schema), the description explains the purpose and techniques but does not detail how parameters interact or what the output format is. It states the output is an 'optimized prompt', which is adequate but could be more specific.

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?

All 14 parameters have descriptions in the schema (100% coverage), so the tool description adds limited value beyond the schema. However, the description provides context about the strategies (e.g., chain-of-thought), which aligns with parameters like enableChainOfThought, adding some semantic 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 tool transforms user requests into optimized prompts using specific techniques. However, it does not differentiate from sibling tools like 'analyze-request' or 'quick-enhance', leaving ambiguity about when to use this tool versus alternatives.

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 says 'Perfect for enhancing any request', implying universal applicability, but provides no explicit guidance on when to use this tool versus siblings or when not to use it. No exclusion criteria or context is given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

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/AungMyoKyaw/betterprompt-mcp'

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