RT-Prompt-MCP
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 |
|---|---|
| get_backend_suggestionsD | – |
| get_frontend_suggestionsD | – |
| get_general_suggestionsD | – |
| get_ui_design_suggestionsD | – |
| get_rt_crud_suggestionsD | – |
| get_feishu_promptD | – |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| info |
TDQS
Scored across 6 tools
The tools are clearly distinguished by their target domains (backend, frontend, general, RT-CRUD, UI design, Feishu), making misselection unlikely. However, the lack of descriptions means the exact boundaries between 'general' and domain-specific suggestions are unclear, which could cause minor confusion.
All tools follow a perfect 'get_[domain]_suggestions' pattern, with consistent snake_case and verb-noun structure. This predictability makes it easy for agents to understand and use the toolset without naming-related errors.
Six tools is a reasonable number for a prompt suggestion server, covering multiple domains without being overwhelming. It could be slightly thin if more granular domains are needed, but the scope appears well-defined for common development areas.
The toolset is severely incomplete as it only provides 'get' operations with no ability to create, update, delete, or manage prompts. For a prompt management server, this represents significant gaps that will limit agent workflows and cause dead ends in multi-step tasks.