{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-pv-bhat-vibe-check-mcp-server","slug":"mcp-pv-bhat-vibe-check-mcp-server","name":"vibe-check-mcp-server","type":"mcp","url":"https://github.com/PV-Bhat/vibe-check-mcp-server","page_url":"https://unfragile.ai/mcp-pv-bhat-vibe-check-mcp-server","categories":["mcp-servers"],"tags":["agentic-ai","agentic-workflow","ai-agents","chain-of-thought","cpi","error-handling","mcp","mcp-server","model-context-protocol","rli","vibe-coding","workflow-automation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-pv-bhat-vibe-check-mcp-server__cap_0","uri":"capability://planning.reasoning.pattern.inertia.detection.via.metacognitive.questioning","name":"pattern-inertia detection via metacognitive questioning","description":"Analyzes an AI agent's current reasoning path against the original user request to identify tunnel vision, scope creep, and over-engineering through structured metacognitive prompts sent to the Gemini API. The vibe_check tool accepts the agent's plan, original request, optional thinking logs, and available tools, then returns pattern-interrupt questions designed to break reasoning lock-in by surfacing hidden assumptions and alternative approaches.","intents":["Detect when my agent has drifted from the original user intent into a more complex adjacent problem","Identify when my agent is stuck in a single reasoning path and needs to consider alternatives","Get specific questions to ask my agent to break out of over-engineering patterns","Understand what assumptions my agent is making that might be incorrect"],"best_for":["Teams building long-horizon agentic workflows where agents tend to scope-creep","Developers working on high-risk or ambiguous tasks where alignment with user intent is critical","AI engineers debugging agent behavior and reasoning lock-in issues"],"limitations":["Requires Gemini API calls for each vibe_check invocation, adding latency (~1-3 seconds per check)","Effectiveness depends on quality of the original userRequest parameter — vague requests produce vague pattern interrupts","No built-in persistence of pattern detection history across agent sessions without external storage","Limited to text-based reasoning analysis; cannot directly inspect agent internal state or embeddings"],"requires":["Google Gemini API key with access to gemini-2.0-flash or compatible model","MCP client integration (Claude, or custom MCP-compatible application)","TypeScript/Node.js runtime for the MCP server itself"],"input_types":["text (current plan/reasoning from agent)","text (original user request)","text (optional thinking log showing agent's reasoning steps)","text (optional list of available tools)","text (optional focus areas for pattern detection)"],"output_types":["structured text (pattern-interrupt questions)","text (identified assumptions)","text (alternative approaches suggested)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pv-bhat-vibe-check-mcp-server__cap_1","uri":"capability://planning.reasoning.plan.simplification.and.over.engineering.prevention","name":"plan simplification and over-engineering prevention","description":"The vibe_distill tool accepts a complex agent plan and uses Gemini API to extract essential elements, identify unnecessary abstractions, and generate a simplified version that preserves core functionality while removing scope creep. It analyzes the plan's complexity, identifies over-engineered components, and returns both a distilled plan and a rationale explaining what was removed and why.","intents":["Simplify an overly complex agent plan before execution to reduce implementation time and risk","Identify which parts of my agent's proposed solution are actually necessary vs nice-to-have","Get a minimal viable version of my agent's plan that still solves the core problem","Understand what abstractions my agent added that could be removed"],"best_for":["Developers building coding agents that tend to propose enterprise-scale solutions for simple problems","Teams working on time-constrained projects where MVP-first approach is critical","AI engineers optimizing agent efficiency by reducing unnecessary planning steps"],"limitations":["Gemini API calls required for each distillation, adding ~1-3 seconds latency","May over-simplify domain-specific requirements if the original plan doesn't explicitly state why complexity is necessary","No domain-specific knowledge of the problem space — relies entirely on what's in the plan text","Cannot preserve intentional complexity that serves non-functional requirements (e.g., maintainability, extensibility)"],"requires":["Google Gemini API key with access to gemini-2.0-flash or compatible model","MCP client integration","TypeScript/Node.js runtime"],"input_types":["text (complex plan or proposed solution)","text (optional context about constraints or requirements)","text (optional list of available tools/resources)"],"output_types":["text (simplified plan)","text (rationale for removals)","structured text (core vs optional components identified)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pv-bhat-vibe-check-mcp-server__cap_10","uri":"capability://memory.knowledge.thinking.log.accumulation.and.reasoning.trajectory.analysis","name":"thinking-log accumulation and reasoning trajectory analysis","description":"Accepts and accumulates thinking logs from agent reasoning steps, enabling vibe_check to analyze the full reasoning trajectory rather than isolated snapshots. The thinking log parameter allows agents to pass their step-by-step reasoning, which vibe_check uses to understand how the agent arrived at its current plan and identify where reasoning diverged from the original intent. Supports optional phase tracking to understand which stage of reasoning the agent is in.","intents":["Help vibe-check understand how my agent arrived at its current plan by showing the reasoning steps","Detect when my agent's reasoning diverged from the original intent early in the process","Identify specific reasoning steps that led to over-engineering or tunnel vision","Track which phase of reasoning the agent is in (planning, implementation, optimization, etc.)"],"best_for":["Developers debugging complex agent reasoning and want to understand the full trajectory","Teams building agents that expose their thinking process for oversight","AI engineers analyzing where agents make incorrect reasoning decisions"],"limitations":["Requires agents to explicitly log their thinking; not all agents expose this information","Thinking logs can become very large for long reasoning chains, hitting token limits with Gemini","No automatic thinking log generation; agents must be instrumented to produce logs","Log quality depends on how detailed the agent's thinking is; sparse logs provide limited insight"],"requires":["Agent that exposes thinking logs or reasoning steps","Optional: phase tracking mechanism to identify reasoning stage","Google Gemini API key"],"input_types":["text (thinking log: accumulated reasoning steps from agent)","text (optional phase: current stage of reasoning)","text (current plan or step)"],"output_types":["text (analysis of reasoning trajectory)","text (identified divergence points from original intent)","text (pattern-interrupt questions based on reasoning history)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pv-bhat-vibe-check-mcp-server__cap_11","uri":"capability://planning.reasoning.confidence.level.tracking.and.uncertainty.quantification","name":"confidence-level tracking and uncertainty quantification","description":"Accepts optional confidence level parameters in vibe_check calls to track how certain the agent is about its current plan. Enables vibe_check to calibrate its pattern-interrupt intensity based on confidence — low-confidence plans receive more aggressive questioning, while high-confidence plans receive lighter oversight. Supports both explicit confidence scores and implicit confidence inference from the plan description.","intents":["Adjust vibe-check intensity based on how confident my agent is about its plan","Get more aggressive pattern interrupts when my agent is uncertain and exploring options","Reduce overhead when my agent is highly confident and has already validated its approach","Track agent confidence evolution across multiple vibe_check calls in a session"],"best_for":["Teams building agents that expose confidence metrics or uncertainty estimates","Developers who want to calibrate oversight intensity based on agent certainty","AI engineers optimizing the balance between oversight and agent autonomy"],"limitations":["Requires agents to provide confidence scores; not all agents expose this information","Confidence calibration is heuristic-based; no validation that confidence scores are accurate","No mechanism to validate whether agent's confidence is justified or overconfident","Confidence-based intensity adjustment may miss important patterns in high-confidence plans"],"requires":["Agent that provides confidence scores or uncertainty estimates","Optional: confidence threshold configuration","Google Gemini API key"],"input_types":["numeric (confidence level: 0-1 or 0-100 scale)","text (optional confidence rationale)","text (current plan)"],"output_types":["text (pattern-interrupt questions calibrated to confidence level)","text (confidence assessment and validation)","structured data (confidence tracking across session)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pv-bhat-vibe-check-mcp-server__cap_12","uri":"capability://planning.reasoning.focus.area.specification.and.targeted.pattern.detection","name":"focus-area specification and targeted pattern detection","description":"Accepts optional focusAreas parameter that allows users to specify which aspects of the agent's plan should receive heightened pattern detection scrutiny (e.g., 'database design', 'API architecture', 'error handling'). Vibe_check uses these focus areas to concentrate its pattern-interrupt questions on the specified domains rather than analyzing the entire plan uniformly. Enables domain-specific oversight without requiring domain expertise in the system.","intents":["Focus vibe-check's pattern detection on specific aspects of my agent's plan that I'm concerned about","Get targeted questions about database design, API architecture, error handling, etc. without generic oversight","Reduce noise from pattern detection by excluding areas I'm confident about","Concentrate oversight on high-risk or unfamiliar domains"],"best_for":["Teams who know which aspects of their agent's plans tend to have problems","Developers who want to focus oversight on specific technical domains","Organizations with domain expertise who want to guide pattern detection"],"limitations":["Requires users to specify focus areas; no automatic detection of problem domains","Focus areas are text-based; no semantic understanding of domain relationships","Focusing on specific areas may miss patterns in other parts of the plan","No validation that specified focus areas are actually relevant to the plan"],"requires":["List of focus areas (text descriptions of domains to scrutinize)","Google Gemini API key"],"input_types":["text array (focus areas: 'database design', 'API architecture', etc.)","text (current plan)"],"output_types":["text (pattern-interrupt questions focused on specified areas)","text (analysis of focus areas)","structured data (pattern detection results per focus area)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pv-bhat-vibe-check-mcp-server__cap_2","uri":"capability://memory.knowledge.pattern.learning.and.feedback.loop.integration","name":"pattern learning and feedback loop integration","description":"The vibe_learn tool maintains a pattern database of recurring reasoning mistakes and over-engineering patterns observed across agent sessions. It accepts feedback about what went wrong (e.g., 'agent over-engineered the database schema'), stores it with context, and makes this pattern history available to vibe_check for future sessions. This creates a self-improving feedback loop where the system learns from past agent failures.","intents":["Record patterns of mistakes my agent makes so I can detect them earlier in future runs","Build institutional knowledge about what kinds of over-engineering my agent tends toward","Improve vibe_check's pattern detection by feeding it historical failure patterns","Track which types of tasks trigger tunnel vision in my agent"],"best_for":["Teams running repeated agent workflows where pattern learning compounds over time","Organizations building long-lived agentic systems that need to improve through experience","Developers who want to instrument their agents with self-improving oversight"],"limitations":["Pattern database is in-memory or requires external persistence layer — no built-in database included","Pattern matching is text-based and heuristic; no semantic similarity matching between patterns","Requires manual feedback loop — patterns must be explicitly logged by the user or agent wrapper","No automatic pattern pruning or expiration; old patterns may become stale or irrelevant"],"requires":["Google Gemini API key","MCP client integration","External storage solution if persistence across server restarts is needed (e.g., database, file system)","TypeScript/Node.js runtime"],"input_types":["text (pattern description: what went wrong)","text (context about the task or agent state when pattern occurred)","text (optional severity or frequency indicator)"],"output_types":["structured data (pattern stored in database)","text (confirmation of pattern recorded)","text (list of similar patterns already known)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pv-bhat-vibe-check-mcp-server__cap_3","uri":"capability://tool.use.integration.mcp.compliant.tool.exposure.and.request.routing","name":"mcp-compliant tool exposure and request routing","description":"Implements a Model Context Protocol (MCP) server that exposes the three vibe_check tools (vibe_check, vibe_distill, vibe_learn) as callable resources to MCP-compatible clients like Claude. The server handles MCP request validation, parameter extraction, tool routing, Gemini API integration, and response formatting according to MCP specification. Built on the MCP SDK with TypeScript, it manages the full request-response lifecycle.","intents":["Integrate vibe-check capabilities directly into Claude or other MCP-compatible AI agents","Expose vibe-check tools as native functions that agents can call during reasoning","Enable seamless integration with existing MCP client ecosystems without custom API wrappers","Deploy vibe-check as a standalone service that multiple clients can connect to"],"best_for":["Teams using Claude or other MCP-compatible AI clients who want to add metacognitive oversight","Developers building custom MCP servers who want to integrate vibe-check capabilities","Organizations deploying agentic systems that need standardized tool interfaces"],"limitations":["MCP protocol overhead adds ~50-200ms per request compared to direct function calls","Requires MCP-compatible client; cannot be used with non-MCP AI systems without custom wrapper","Server must be running and accessible to client; no built-in load balancing or failover","Tool responses are limited to MCP-compatible data types (text, structured data); no streaming responses"],"requires":["Node.js 18+ runtime","MCP SDK (@modelcontextprotocol/sdk)","Google Gemini API key","MCP-compatible client (Claude, custom MCP client, or Smithery integration)","TypeScript compiler or Node.js with TypeScript support"],"input_types":["MCP tool call requests (JSON-RPC format)","tool parameters (text, structured data)"],"output_types":["MCP tool response (JSON-RPC format)","text (tool results)","structured data (analysis results)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pv-bhat-vibe-check-mcp-server__cap_4","uri":"capability://tool.use.integration.gemini.api.integration.with.streaming.and.error.handling","name":"gemini api integration with streaming and error handling","description":"Abstracts all interactions with Google's Gemini API (gemini-2.0-flash model) behind a unified integration layer that handles API authentication, request formatting, response parsing, error handling, and retry logic. The integration accepts prompts and context from the three vibe_check tools, sends them to Gemini, and returns structured responses. Includes error handling for API failures, rate limiting, and invalid responses.","intents":["Reliably call Gemini API for pattern detection and plan analysis without managing authentication","Handle API errors and rate limiting gracefully without breaking agent workflows","Format prompts and context for Gemini in a way that produces consistent, structured responses","Integrate Gemini capabilities without exposing API complexity to tool implementations"],"best_for":["Developers who want to use Gemini for metacognitive agent oversight without managing API details","Teams deploying vibe-check in production who need robust error handling and retry logic","Organizations already using Gemini who want to leverage it for agent oversight"],"limitations":["Dependent on Gemini API availability and rate limits; no fallback to alternative models","API latency (~1-3 seconds per call) adds overhead to agent reasoning loops","Requires valid Google Cloud credentials and API key; no local/offline mode","Response quality depends on Gemini model version; no guarantee of consistent output format"],"requires":["Google Cloud project with Gemini API enabled","Valid Google API key (GOOGLE_API_KEY environment variable)","Network connectivity to Google API endpoints","@google/generative-ai SDK or equivalent"],"input_types":["text (prompts for Gemini)","text (context: agent plan, user request, thinking logs)","structured data (tool parameters)"],"output_types":["text (Gemini response)","structured data (parsed response from Gemini)","error objects (API errors with retry information)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pv-bhat-vibe-check-mcp-server__cap_5","uri":"capability://memory.knowledge.session.aware.context.management.for.multi.turn.agent.workflows","name":"session-aware context management for multi-turn agent workflows","description":"Maintains session context across multiple vibe_check calls within a single agent workflow, allowing the system to track thinking logs, available tools, focus areas, and previous pattern detections. The vibe_check tool accepts an optional sessionId parameter that enables it to accumulate context across multiple invocations, improving pattern detection accuracy by understanding the full reasoning trajectory rather than isolated snapshots.","intents":["Track my agent's reasoning across multiple steps so vibe-check understands the full context","Detect patterns that only emerge across multiple reasoning steps, not in isolated snapshots","Maintain focus areas and constraints across a long agent workflow","Correlate pattern detections with previous vibe_check calls in the same session"],"best_for":["Teams running multi-step agent workflows where reasoning evolves over time","Developers debugging complex agent behaviors that require understanding the full reasoning trajectory","Organizations building long-horizon agentic tasks where early pattern detection prevents downstream failures"],"limitations":["Session context requires external storage if persistence across server restarts is needed","No automatic session cleanup; old sessions accumulate in memory without TTL management","Context size grows with each vibe_check call; very long sessions may hit token limits with Gemini","No built-in session isolation or multi-tenancy; all sessions share the same pattern database"],"requires":["Session ID generation mechanism (UUID or similar)","Optional external storage for session persistence (database, cache, file system)","Google Gemini API key","MCP client integration"],"input_types":["text (sessionId: identifier for the agent workflow)","text (thinking log: accumulated reasoning steps)","text (current plan/step in the workflow)","text (focus areas or constraints for the session)"],"output_types":["text (pattern-interrupt questions with session context)","structured data (session state with accumulated context)","text (cross-step pattern detections)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pv-bhat-vibe-check-mcp-server__cap_6","uri":"capability://tool.use.integration.claude.integration.via.smithery.configuration","name":"claude integration via smithery configuration","description":"Provides pre-configured Smithery integration that enables Claude to discover and use vibe-check tools without manual MCP setup. The integration includes a Smithery configuration file that registers the vibe-check MCP server, tool definitions, and parameter schemas, allowing Claude users to enable vibe-check oversight with a single configuration step rather than manual MCP server setup.","intents":["Enable Claude users to add vibe-check oversight without understanding MCP protocol details","Provide a one-click integration path for Claude users who want metacognitive agent oversight","Automatically discover vibe-check tools in Claude's tool palette","Simplify deployment by using Smithery's managed MCP infrastructure"],"best_for":["Claude users who want to add vibe-check without technical MCP knowledge","Teams using Claude for agentic workflows who want managed MCP integration","Non-technical users who want to enable agent oversight through configuration"],"limitations":["Requires Smithery account and integration; adds dependency on Smithery's infrastructure","Configuration changes require Smithery re-deployment; not suitable for rapid iteration","Limited to Claude; cannot be used with other MCP clients through Smithery","Smithery configuration may lag behind latest vibe-check tool updates"],"requires":["Smithery account and API access","Claude integration enabled in Smithery","Smithery configuration file (smithery.json or equivalent)","Valid Google Gemini API key"],"input_types":["configuration file (Smithery config with vibe-check server registration)"],"output_types":["Claude tool palette integration","tool definitions and parameter schemas"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pv-bhat-vibe-check-mcp-server__cap_7","uri":"capability://planning.reasoning.assumption.surfacing.and.tunnel.vision.breaking.through.structured.questioning","name":"assumption surfacing and tunnel-vision breaking through structured questioning","description":"Implements a structured questioning framework that generates targeted metacognitive questions designed to surface hidden assumptions in agent reasoning and break tunnel vision. The vibe_check tool uses Gemini to analyze the agent's plan, identify implicit assumptions (e.g., 'assumes the database must be normalized'), and generate questions that challenge those assumptions (e.g., 'Have you considered a simpler denormalized schema?'). Questions are contextualized to the specific plan and original user request.","intents":["Identify hidden assumptions my agent is making that might be incorrect","Generate specific questions that break my agent out of tunnel vision","Challenge over-engineered design decisions by surfacing the assumptions behind them","Help my agent see alternative approaches it hasn't considered"],"best_for":["Developers debugging agent reasoning and want to understand implicit assumptions","Teams working on ambiguous or high-risk tasks where assumption validation is critical","AI engineers building agents that need to justify their design decisions"],"limitations":["Question quality depends on Gemini's ability to infer assumptions from text; subtle assumptions may be missed","Generated questions are generic patterns; may not catch domain-specific assumptions","No mechanism to validate whether agent actually considers the questions or changes behavior","Requires clear, detailed plan descriptions; vague plans produce vague questions"],"requires":["Google Gemini API key","Clear description of agent's current plan","Original user request for alignment checking","Optional: thinking log showing agent's reasoning steps"],"input_types":["text (agent's current plan or reasoning)","text (original user request)","text (optional thinking log)","text (optional list of available tools)"],"output_types":["text (structured questions targeting identified assumptions)","text (rationale for each question)","text (alternative approaches suggested)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pv-bhat-vibe-check-mcp-server__cap_8","uri":"capability://planning.reasoning.complexity.analysis.and.essential.element.extraction","name":"complexity analysis and essential-element extraction","description":"Analyzes agent-generated plans to identify complexity hotspots, categorize components as essential vs optional, and extract the minimal viable solution. The vibe_distill tool uses Gemini to parse the plan, identify unnecessary abstractions, redundant components, and scope creep, then generates a simplified version with explicit rationale for what was removed. Includes categorization of components by criticality and impact.","intents":["Understand which parts of my agent's plan are actually essential vs nice-to-have","Get a minimal viable version of the plan that still solves the core problem","Identify unnecessary abstractions or over-engineering in the proposed solution","Reduce implementation complexity and time by removing non-essential components"],"best_for":["Teams building coding agents that tend to propose enterprise-scale solutions","Developers working on time-constrained projects where MVP-first is critical","AI engineers optimizing agent efficiency by reducing unnecessary planning steps"],"limitations":["Simplification is heuristic-based; may remove components that are actually necessary for non-functional requirements","No domain-specific knowledge; relies entirely on what's in the plan text","Cannot preserve intentional complexity that serves maintainability, extensibility, or other non-functional goals","Gemini's analysis may miss subtle dependencies between components"],"requires":["Google Gemini API key","Detailed plan or proposed solution from agent","Optional: context about constraints or requirements"],"input_types":["text (complex plan or proposed solution)","text (optional context about constraints)","text (optional list of available tools/resources)"],"output_types":["text (simplified plan)","text (rationale for removals)","structured data (components categorized by criticality)","text (estimated complexity reduction)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pv-bhat-vibe-check-mcp-server__cap_9","uri":"capability://automation.workflow.docker.deployment.and.containerized.mcp.server.hosting","name":"docker deployment and containerized mcp server hosting","description":"Provides Docker configuration for containerized deployment of the vibe-check MCP server, enabling easy deployment to cloud platforms, Kubernetes clusters, or local Docker environments. Includes Dockerfile with optimized Node.js runtime, environment variable configuration for API keys, and health check endpoints. Supports both direct Docker deployment and orchestration through container platforms.","intents":["Deploy vibe-check as a containerized service without managing Node.js dependencies","Run vibe-check in production environments with container orchestration","Scale vibe-check horizontally by deploying multiple container instances","Simplify CI/CD integration by using Docker images instead of direct Node.js deployment"],"best_for":["Teams deploying vibe-check to cloud platforms (AWS, GCP, Azure) using containers","Organizations using Kubernetes or Docker Compose for service orchestration","DevOps teams who prefer containerized deployments over direct Node.js installation"],"limitations":["Requires Docker runtime; adds container overhead (~50-100ms startup time per request)","No built-in load balancing or auto-scaling; requires external orchestration","Container image size may be large if including all dependencies; no multi-stage build optimization mentioned","Requires managing API key secrets in container environment; no built-in secret management"],"requires":["Docker runtime (Docker Desktop, Docker Engine, or equivalent)","Google Gemini API key (passed as environment variable)","Optional: container orchestration platform (Kubernetes, Docker Compose, ECS, etc.)","Optional: container registry for storing images"],"input_types":["Dockerfile configuration","environment variables (GOOGLE_API_KEY, port configuration)","MCP client requests (same as non-containerized deployment)"],"output_types":["Docker image","running container with MCP server","container logs and health check responses"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Google Gemini API key with access to gemini-2.0-flash or compatible model","MCP client integration (Claude, or custom MCP-compatible application)","TypeScript/Node.js runtime for the MCP server itself","MCP client integration","TypeScript/Node.js runtime","Agent that exposes thinking logs or reasoning steps","Optional: phase tracking mechanism to identify reasoning stage","Google Gemini API key","Agent that provides confidence scores or uncertainty estimates","Optional: confidence threshold configuration"],"failure_modes":["Requires Gemini API calls for each vibe_check invocation, adding latency (~1-3 seconds per check)","Effectiveness depends on quality of the original userRequest parameter — vague requests produce vague pattern interrupts","No built-in persistence of pattern detection history across agent sessions without external storage","Limited to text-based reasoning analysis; cannot directly inspect agent internal state or embeddings","Gemini API calls required for each distillation, adding ~1-3 seconds latency","May over-simplify domain-specific requirements if the original plan doesn't explicitly state why complexity is necessary","No domain-specific knowledge of the problem space — relies entirely on what's in the plan text","Cannot preserve intentional complexity that serves non-functional requirements (e.g., maintainability, extensibility)","Requires agents to explicitly log their thinking; not all agents expose this information","Thinking logs can become very large for long reasoning chains, hitting token limits with Gemini","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.35673925984359545,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:44.761Z","last_commit":"2026-03-30T17:06:12Z"},"community":{"stars":484,"forks":63,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-pv-bhat-vibe-check-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=mcp-pv-bhat-vibe-check-mcp-server"}},"signature":"2heNWVz7S9wZbcmB8WwQnTOQgNmynfBdYpvPxqj9p9hPMyM3c09/a8E4+04QmQy85uaUBmsAdcxGwT6E4j1gDA==","signedAt":"2026-06-19T10:08:30.350Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-pv-bhat-vibe-check-mcp-server","artifact":"https://unfragile.ai/mcp-pv-bhat-vibe-check-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-pv-bhat-vibe-check-mcp-server","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}