A
licenseA
qualityB
maintenanceA Model Context Protocol server that empowers AI agents with metacognitive monitoring to detect reasoning loops and provide intelligent recovery using case-based reasoning and statistical analysis.
9
12 npm
9
MIT
Vibe Check MCP
by PV-Bhat
🧠 Comprobación de vibración MCP
También puede encontrar Vibecheck en: mcpservers.org , Glama.ai , mcp.so
El patito de goma interno de tu IA cuando no puede patita de goma por sí solo.
¿Qué es Vibe Check?
En la era de la "codificación de vibraciones" , los agentes de IA ahora tienen capacidades increíbles, pero la pregunta ahora se ha trasladado:
de
"¿Puede mi agente de IA realmente realizar esta compleja tarea ?"
a
"¿Puede mi agente de IA entender que quiero escribir un programa simple , no una infraestructura para una empresa tecnológica multimillonaria ?"
Proporciona el momento esencial de "¡Espera... esto no es!" que los agentes de IA actualmente no tienen: una capa de supervisión autocorrectiva integrada. Es el servidor MCP definitivo para la comprobación de la cordura de Vibe Coder.
Evite errores en cascada en los flujos de trabajo de IA implementando interrupciones de patrones estratégicos.
Utiliza la herramienta llamada "Vibe Check" con LearnLM 1.5 Pro (Gemini API), optimizada para la pedagogía y la metacognición para mejorar la estrategia de flujo de trabajo compleja y evitar errores de visión de túnel.
Implementa "Vibe Distill" para fomentar la simplificación del plan, evitar soluciones de ingeniería excesiva y minimizar la deriva contextual en los agentes.
Bucles de retroalimentación de automejora: los agentes pueden registrar errores en "Vibe Learn" para mejorar la recuperación semántica y ayudar a la IA de supervisión a identificar patrones a lo largo del tiempo.
TLDR: Implemente un agente optimizado para detener a su agente y hacerle reconsiderar antes de que implemente con confianza algo incorrecto.
Related MCP server: Visum Thinker MCP Server
El problema: la inercia del patrón
En el movimiento de codificación vibrante, todos usamos LLM para generar, refactorizar y depurar nuestro código. Pero estos modelos tienen un defecto crítico: una vez que comienzan a seguir un camino de razonamiento, continúan incluso si este es claramente erróneo.
You: "Parse this CSV file"
AI: "First, let's implement a custom lexer/parser combination that can handle arbitrary
CSV dialects with an extensible architecture for future file formats..."
You: *stares at 200 lines of code when you just needed to read 10 rows*Esta inercia del patrón conduce a:
🔄 Visión de túnel : Su agente se queda estancado en un enfoque, incapaz de ver alternativas.
📈 Aumento del alcance : las tareas simples evolucionan gradualmente hasta convertirse en soluciones a escala empresarial
🔌 Sobreingeniería : Añadir capas de abstracción a problemas que no las necesitan
❓ Desalineación : Resolver un problema adyacente pero diferente al que usted solicitó
Características: Herramientas de supervisión metacognitiva
Vibe Check agrega una capa metacognitiva a los flujos de trabajo de sus agentes con tres herramientas integradas:
🛑 comprobación de vibraciones
Mecanismo de interrupción de patrones que rompe la visión de túnel con el cuestionamiento metacognitivo:
vibe_check({
"phase": "planning", // planning, implementation, or review
"userRequest": "...", // FULL original user request
"plan": "...", // Current plan or thinking
"confidence": 0.7 // Optional: 0-1 confidence level
})⚓ vibra_distill
Punto de anclaje del metapensamiento que recalibra flujos de trabajo complejos:
vibe_distill({
"plan": "...", // Detailed plan to simplify
"userRequest": "..." // FULL original user request
})🔄 vibra_aprender
Bucle de retroalimentación de automejora que genera reconocimiento de patrones a lo largo del tiempo:
vibe_learn({
"mistake": "...", // One-sentence description of mistake
"category": "...", // From standard categories
"solution": "..." // How it was corrected
})Vibe Check en acción
Antes de comprobar la vibración:
Claude asume el significado de MCP a pesar de la ambigüedad, lo que lleva a que todos los pasos posteriores tengan esta suposición errónea.
Después de comprobar la vibración:
Se llama Vibe Check MCP y señala la ambigüedad, lo que obliga a Claude a reconocer esta falta de información y abordarla de manera proactiva.
Instalación y configuración
Instalación mediante herrería
Para instalar vibe-check-mcp-server para Claude Desktop automáticamente a través de Smithery :
npx -y @smithery/cli install @PV-Bhat/vibe-check-mcp-server --client claudeInstalación manual mediante npm (recomendado)
# Clone the repo
git clone https://github.com/PV-Bhat/vibe-check-mcp-server.git
cd vibe-check-mcp-server
# Install dependencies
npm install
# Build the project
npm run build
# Start the server
npm run startIntegración con Claude
Añade a tu claude_desktop_config.json :
"vibe-check": {
"command": "node",
"args": [
"/path/to/vibe-check-mcp/build/index.js"
],
"env": {
"GEMINI_API_KEY": "YOUR_GEMINI_API_KEY"
}
}Configuración del entorno
Cree un archivo .env en la raíz del proyecto:
GEMINI_API_KEY=your_gemini_api_key_hereGuía de indicaciones para agentes
Para lograr interrupciones de patrón efectivas, incluya estas instrucciones en el indicador de su sistema:
As an autonomous agent, you will:
1. Treat vibe_check as a critical pattern interrupt mechanism
2. ALWAYS include the complete user request with each call
3. Specify the current phase (planning/implementation/review)
4. Use vibe_distill as a recalibration anchor when complexity increases
5. Build the feedback loop with vibe_learn to record resolved issuesCuándo utilizar cada herramienta
Herramienta | Cuándo utilizarlo |
🛑 comprobación de vibraciones | Cuando su agente comienza a explicar los fundamentos de blockchain para una aplicación de tareas pendientes |
⚓ vibra_distill | Cuando el plan de su agente tiene más viñetas anidadas que toda su especificación técnica |
🔄 vibra_aprender | Después de haber guiado manualmente a su agente para que salga del abismo de la complejidad |
Referencia de API
Consulte la Referencia técnica para obtener la documentación completa de la API.
Arquitectura
Vibe Check implementa una arquitectura metacognitiva de doble capa basada en principios de supervisión recursiva. Ideas clave:
Resistencia a la inercia del patrón : los agentes LLM demuestran naturalmente una propiedad similar al impulso en sus caminos de razonamiento, lo que requiere una intervención externa para redirigirlos.
Interrupciones de resonancia de fase : el cuestionamiento metacognitivo debe alinearse con la fase actual del agente (planificación/implementación/revisión) para lograr el máximo impacto correctivo.
Integración de la estructura de autoridad : se debe instar explícitamente a los agentes a tratar la retroalimentación metacognitiva externa como interrupciones de alta prioridad en lugar de sugerencias opcionales.
Mecanismos de compresión de anclajes : los flujos de razonamiento complejos deben destilarse en cadenas de anclaje mínimas que sirvan como puntos de recalibración efectivos.
Bucles de retroalimentación recursivos : todos los errores observados deben almacenarse y aprovecharse para construir modelos de fallas longitudinales que mejoren la eficacia de las interrupciones.
Para obtener más detalles sobre los principios de diseño subyacentes, consulte Filosofía .
Vibe Check en acción (continuación)
Documentación
Documento | Descripción |
Técnicas detalladas para la integración de agentes | |
Encadenamiento de retroalimentación, niveles de confianza y más | |
Documentación completa de la API | |
Los principios de alineación de IA más profundos detrás de Vibe Check | |
Ejemplos reales de Vibe Check en acción |
Contribuyendo
¡Agradecemos tus contribuciones a Vibe Check! Ya sea para corregir errores, añadir funciones o simplemente mejorar la documentación, consulta nuestras Pautas de Contribución para empezar.
Licencia
Available Tools
2 toolsvibe_checkB
Metacognitive questioning tool that identifies assumptions and breaks tunnel vision to prevent cascading errors
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| goal | Yes | The agent's current goal | |
| modelOverride | No | ||
| plan | Yes | The agent's detailed plan | |
| progress | No | The agent's progress so far | |
| sessionId | No | Optional session ID for state management | |
| taskContext | No | The context of the current task | |
| uncertainties | No | The agent's uncertainties | |
| userPrompt | No | The original user prompt |
TDQS
B3.2/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool's cognitive effects (identifying assumptions, breaking tunnel vision, preventing errors) but lacks details on how it operates (e.g., does it generate questions, provide feedback, modify plans?), what it returns, or any constraints like rate limits or permissions. This leaves significant gaps in understanding its behavior.
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 a single, efficient sentence that front-loads the key purpose ('metacognitive questioning tool') and elaborates with clear outcomes. Every word earns its place, avoiding redundancy or fluff, making it highly concise and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (8 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the tool returns, how it uses the parameters (e.g., 'modelOverride' for AI model selection), or behavioral details like state management with 'sessionId.' For a metacognitive tool with rich inputs, more context is needed to guide effective use.
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?
The schema description coverage is high (88%), so the schema already documents most parameters well (e.g., 'goal,' 'plan,' 'uncertainties'). The description doesn't add specific meaning beyond the schema, such as explaining how parameters like 'modelOverride' or 'sessionId' relate to the tool's purpose. Baseline 3 is appropriate as the schema does the heavy lifting, but no extra value is provided.
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's purpose as a 'metacognitive questioning tool' that 'identifies assumptions and breaks tunnel vision to prevent cascading errors.' It uses specific verbs ('identifies,' 'breaks,' 'prevent') and describes the cognitive function, though it doesn't explicitly differentiate from its sibling 'vibe_learn' beyond the general domain of 'vibe' tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage in scenarios involving assumptions, tunnel vision, or error prevention, suggesting it's for reflective or corrective moments. However, it doesn't provide explicit guidance on when to use this tool versus 'vibe_learn' or other alternatives, nor does it specify prerequisites or exclusions, leaving the context somewhat open-ended.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
vibe_learnC
Pattern recognition system that tracks common errors and solutions to prevent recurring issues
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| category | Yes | Category (standard categories: Complex Solution Bias, Feature Creep, Premature Implementation, Misalignment, Overtooling, Preference, Success, Other) | |
| mistake | Yes | One-sentence description of the learning entry | |
| sessionId | No | Optional session ID for state management | |
| solution | No | How it was corrected (if applicable) | |
| type | No | Type of learning entry |
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions tracking and prevention but fails to detail critical aspects like whether this is a read/write operation, data persistence, permissions needed, or error handling. This leaves significant gaps for a tool with 5 parameters and potential data mutation.
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 a single, efficient sentence that directly states the tool's purpose without redundancy or unnecessary details. It is front-loaded and appropriately sized for its informational content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, output expectations, and differentiation from siblings, making it inadequate for guiding an agent in practical use beyond a high-level purpose.
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?
The schema description coverage is 100%, providing clear documentation for all 5 parameters. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline of 3 without compensating or enhancing the schema's information.
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's purpose as a 'pattern recognition system that tracks common errors and solutions to prevent recurring issues,' which specifies the verb (tracks) and resource (errors/solutions). However, it doesn't explicitly differentiate from its sibling 'vibe_check,' leaving room for ambiguity about their distinct roles.
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 provides no guidance on when to use this tool versus alternatives, including its sibling 'vibe_check.' It lacks context about prerequisites, timing, or exclusions, leaving the agent to infer usage based on the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
v1.0.0- First observed
vibe_check - First observed
vibe_learn
TDQS
B3.3/5.0
Scored across 2 tools
Disambiguation5/5
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.
Naming Consistency5/5
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.
Tool Count3/5
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.
Completeness3/5
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.
Maintenance
ActivitySlowing
ResponsivenessSlow
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