PromptTuner MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| LLM_MODEL | No | Optional: override default model for the chosen provider | |
| LLM_PROVIDER | No | Choose LLM provider (openai, anthropic, or google) | openai |
| GOOGLE_API_KEY | No | Google API key for Gemini models (Gemini 2.0 Flash, Gemini 1.5 Pro) - Get from https://aistudio.google.com/apikey | |
| OPENAI_API_KEY | No | OpenAI API key for GPT models (gpt-4o, gpt-4o-mini, gpt-4-turbo) - Get from https://platform.openai.com/api-keys | |
| ANTHROPIC_API_KEY | No | Anthropic API key for Claude models (Claude 3.5 Sonnet/Haiku) - Get from https://console.anthropic.com |
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
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": true
} |
| resources | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| fix_promptB | Polish and refine a prompt for better clarity, readability, and flow. |
| boost_promptB | Transform a prompt using prompt engineering best practices for maximum clarity and effectiveness. |
| crafting_promptB | Generate a structured, reusable workflow prompt for complex tasks based on a raw request and a few settings. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| fix-prompt | Template for fixing grammar and clarity |
| boost-prompt | Template for boosting prompt effectiveness |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
The three tools have overlapping purposes in prompt improvement, with 'boost_prompt' and 'fix_prompt' both focusing on clarity and effectiveness, which could cause confusion. However, 'crafting_prompt' is more distinct as it generates structured workflows, providing some differentiation.
All tool names follow a consistent verb_noun pattern with clear, descriptive verbs ('boost', 'crafting', 'fix') and the same noun ('prompt'), making them predictable and easy to understand.
With only 3 tools, the server feels thin for a domain like prompt tuning, which might involve more operations such as evaluating prompts, testing variations, or managing prompt libraries. This limited set could restrict agent capabilities.
The toolset is incomplete for prompt tuning, missing essential operations like evaluating prompt effectiveness, comparing different versions, or storing/retrieving prompts. This creates gaps that could lead to agent failures in comprehensive prompt management tasks.