Vibe Check MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| DEFAULT_MODEL | No | Optional override for the default model | gemini-2.5-pro |
| GEMINI_API_KEY | Yes | Your Gemini API key | |
| OPENAI_API_KEY | No | Optional OpenAI API key | |
| OPENROUTER_API_KEY | No | Optional OpenRouter API key | |
| DEFAULT_LLM_PROVIDER | No | Optional override for the default LLM provider | gemini |
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 |
|---|---|
| vibe_checkB | Metacognitive questioning tool that identifies assumptions and breaks tunnel vision to prevent cascading errors |
| vibe_learnC | Pattern recognition system that tracks common errors and solutions to prevent recurring issues |
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 2 tools
The two tools have clearly distinct purposes: vibe_check focuses on metacognitive questioning to prevent immediate errors by identifying assumptions, while vibe_learn focuses on pattern recognition to prevent recurring issues by tracking errors and solutions. There is no overlap or ambiguity between them.
Both tools follow a consistent 'vibe_' prefix pattern with descriptive suffixes (check and learn), making them predictable and readable. The naming style is uniform throughout the set.
With only 2 tools, the set feels thin for a server named 'Vibe Check MCP', which suggests a broader scope for metacognitive or error-prevention functionality. While the tools are well-defined, the count is borderline low for typical MCP server purposes.
The tools cover two key aspects of error prevention (immediate and recurring), but there are notable gaps such as tools for applying learned patterns, adjusting strategies based on feedback, or integrating with external systems. The surface is functional but not fully comprehensive for the inferred domain.