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
🧠 Stimmungscheck MCP
Vibecheck finden Sie auch auf: mcpservers.org , Glama.ai , mcp.so
Die innere Gummiente Ihrer KI, wenn sie sich nicht selbst wie eine Gummiente bewegen kann.
Was ist Vibe Check?
Im Zeitalter des „Vibe Coding“ verfügen KI-Agenten mittlerweile über unglaubliche Fähigkeiten, doch die Frage hat sich inzwischen verschoben:
aus
„Kann mein KI-Agent diese komplexe Aufgabe wirklich erledigen?“
Zu
„Kann mein KI-Agent verstehen, dass ich ein einfaches Programm schreiben möchte und keine Infrastruktur für ein milliardenschweres Technologieunternehmen ?“
Es bietet den entscheidenden „Moment mal, das ist es nicht“-Moment, den KI-Agenten derzeit nicht haben: eine integrierte, selbstkorrigierende Überwachungsebene. Es ist der ultimative MCP-Server für die Sanity-Checks von Vibe Coder:
Verhindern Sie kaskadierende Fehler in KI-Workflows, indem Sie strategische Musterunterbrechungen implementieren.
Verwendet das Tool „Vibe Check“ mit LearnLM 1.5 Pro (Gemini API), das auf Pädagogik und Metakognition abgestimmt ist, um komplexe Workflow-Strategien zu verbessern und Tunnelblickfehler zu vermeiden.
Implementiert „Vibe Distill“, um die Vereinfachung von Plänen zu fördern, übermäßige technische Lösungen zu verhindern und kontextuelle Abweichungen bei Agenten zu minimieren.
Selbstverbessernde Feedbackschleifen: Agenten können Fehler in „Vibe Learn“ protokollieren, um das semantische Erinnerungsvermögen zu verbessern und der Überwachungs-KI dabei zu helfen, Muster im Laufe der Zeit zu erkennen.
TLDR: Implementieren Sie einen Agenten, der so fein abgestimmt ist, dass er Ihren Agenten stoppt und ihn zum Überdenken bringt, bevor er selbstbewusst etwas Falsches implementiert.
Related MCP server: Visum Thinker MCP Server
Das Problem: Musterträgheit
In der Vibe-Coding-Bewegung nutzen wir alle LLMs zum Generieren, Refactoring und Debuggen unseres Codes. Diese Modelle haben jedoch einen entscheidenden Fehler: Sobald sie einen Denkpfad eingeschlagen haben, bleiben sie auch dann bestehen, wenn dieser eindeutig falsch ist.
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*Diese Musterträgheit führt zu:
🔄 Tunnelblick : Ihr Agent bleibt bei einem Ansatz hängen und kann keine Alternativen erkennen
📈 Scope Creep : Einfache Aufgaben entwickeln sich allmählich zu Lösungen im Unternehmensmaßstab
🔌 Overengineering : Hinzufügen von Abstraktionsebenen zu Problemen, die diese nicht benötigen
❓ Fehlausrichtung : Lösen eines benachbarten, aber anderen Problems als dem, nach dem Sie gefragt haben
Funktionen: Metakognitive Überwachungstools
Vibe Check fügt Ihren Agenten-Workflows mit drei integrierten Tools eine metakognitive Ebene hinzu:
🛑 vibe_check
Musterunterbrechungsmechanismus , der den Tunnelblick mit metakognitiven Fragen durchbricht:
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
})⚓ vibe_distill
Ankerpunkt des Metadenkens, der komplexe Arbeitsabläufe neu kalibriert:
vibe_distill({
"plan": "...", // Detailed plan to simplify
"userRequest": "..." // FULL original user request
})🔄 vibe_learn
Selbstverbessernde Rückkopplungsschleife , die mit der Zeit die Mustererkennung aufbaut:
vibe_learn({
"mistake": "...", // One-sentence description of mistake
"category": "...", // From standard categories
"solution": "..." // How it was corrected
})Vibe Check in Aktion
Vor dem Vibe-Check:
Claude geht trotz Mehrdeutigkeit von der Bedeutung von MCP aus, was dazu führt, dass alle nachfolgenden Schritte von dieser falschen Annahme ausgehen.
Nach dem Vibe-Check:
Vibe Check MCP wird aufgerufen und weist auf die Unklarheit hin, was Claude dazu zwingt, diesen Mangel an Informationen anzuerkennen und proaktiv anzugehen
Installation und Einrichtung
Installation über Smithery
So installieren Sie vibe-check-mcp-server für Claude Desktop automatisch über Smithery :
npx -y @smithery/cli install @PV-Bhat/vibe-check-mcp-server --client claudeManuelle Installation über npm (empfohlen)
# 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 startIntegration mit Claude
Fügen Sie zu Ihrer claude_desktop_config.json hinzu:
"vibe-check": {
"command": "node",
"args": [
"/path/to/vibe-check-mcp/build/index.js"
],
"env": {
"GEMINI_API_KEY": "YOUR_GEMINI_API_KEY"
}
}Umgebungskonfiguration
Erstellen Sie eine .env Datei im Projektstammverzeichnis:
GEMINI_API_KEY=your_gemini_api_key_hereLeitfaden zur Agenteneingabe
Um Musterunterbrechungen effektiv zu gestalten, fügen Sie diese Anweisungen in Ihre Systemeingabeaufforderung ein:
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 issuesWann welches Werkzeug verwendet werden soll
Werkzeug | Wann zu verwenden |
🛑 vibe_check | Wenn Ihr Agent beginnt, Blockchain-Grundlagen für eine To-Do-App zu erklären |
⚓ vibe_distill | Wenn der Plan Ihres Agenten mehr verschachtelte Aufzählungspunkte enthält als Ihre gesamte technische Spezifikation |
🔄 vibe_learn | Nachdem Sie Ihren Agenten manuell aus dem Komplexitätsabgrund zurückgeführt haben |
API-Referenz
Die vollständige API-Dokumentation finden Sie in der technischen Referenz .
Architektur
Vibe Check implementiert eine zweischichtige metakognitive Architektur, die auf rekursiven Überwachungsprinzipien basiert. Wichtige Erkenntnisse:
Widerstand gegen Musterträgheit : LLM-Agenten weisen in ihren Denkpfaden von Natur aus eine impulsartige Eigenschaft auf, die zur Umleitung externe Eingriffe erfordert.
Phasenresonante Unterbrechungen : Um eine maximale korrigierende Wirkung zu erzielen, müssen metakognitive Fragen auf die aktuelle Phase des Agenten (Planung/Umsetzung/Überprüfung) abgestimmt sein.
Integration der Autoritätsstruktur : Agenten müssen ausdrücklich dazu aufgefordert werden, externes metakognitives Feedback als Unterbrechungen mit hoher Priorität und nicht als optionale Vorschläge zu behandeln.
Ankerkomprimierungsmechanismen : Komplexe Argumentationsflüsse müssen in minimale Ankerketten destilliert werden, um als effektive Neukalibrierungspunkte zu dienen.
Rekursive Rückkopplungsschleifen : Alle beobachteten Fehltritte müssen gespeichert und genutzt werden, um Längsfehlermodelle zu erstellen, die die Wirksamkeit von Unterbrechungen verbessern.
Weitere Einzelheiten zu den zugrunde liegenden Designprinzipien finden Sie unter Philosophie .
Vibe Check in Aktion (Fortsetzung)
Überprüfungen
Dokumentation
Dokumentieren | Beschreibung |
Detaillierte Techniken zur Agentenintegration | |
Feedback-Verkettung, Vertrauensstufen und mehr | |
Vollständige API-Dokumentation | |
Die tieferen KI-Ausrichtungsprinzipien hinter Vibe Check | |
Praxisbeispiele für Vibe Check in Aktion |
Beitragen
Wir freuen uns über Beiträge zu Vibe Check! Egal, ob es um Fehlerbehebungen, neue Funktionen oder einfach nur um die Verbesserung der Dokumentation geht – lesen Sie unsere Richtlinien für Beiträge, um loszulegen.
Lizenz
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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