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Vibe Check MCP

Maintenance-mode MCP server that adds metacognitive oversight tools for challenging an agent's plan, recording recurring mistakes, and applying session rules before complex or high-risk work continues.

by PV Bhat · submitted by oktofeesh1·added 2026-06-06·
Review first review before installing

Open the source and read safety notes before installing.

Citation facts

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Source URLs
https://github.com/PV-Bhat/vibe-check-mcp-server/blob/main/README.md, https://github.com/PV-Bhat/vibe-check-mcp-server
Brand
Vibe Check MCP
Brand domain
github.com
Safety notes
Vibe Check MCP is in final maintenance mode according to the project README, so check the repository and package state before adopting it broadly., The server is an advisory oversight layer; it can challenge assumptions but cannot guarantee safe, correct, or complete decisions., The `vibe_check` tool sends the goal, plan, optional user prompt, progress, uncertainties, and task context to a configured LLM provider for critique., The `vibe_learn` tool can record mistakes, preferences, successes, categories, and solutions for later reflection., Constitution tools can help apply session rules, but they do not enforce external system permissions or prevent client-side actions by themselves.
Privacy notes
Agent plans, prompts, progress, uncertainties, session identifiers, mistakes, and learned preferences can reveal confidential project details., Provider API keys are loaded from environment variables and should be scoped, stored, and rotated according to local policy., Depending on provider selection, request metadata and prompt content may be processed by Gemini, OpenAI, OpenRouter, Anthropic, or compatible endpoints., Avoid sending secrets, private code, customer data, or regulated information in `vibe_check` or `vibe_learn` inputs unless approved for the selected provider.
Author
PV Bhat
Submitted by
oktofeesh1
Claim status
unclaimed
Last verified
2026-06-06

Decision playbook

Review trust signals before you adopt

Signals are present but mixed. Use the checklist below to confirm the source and operational safety for your environment.

Compare context
Selected

0

Current score

63

Baseline

Delta

No baseline selected

No major trust-signal divergence detected in the current selection.

Source and provenance checks

Needs review

Confirm ownership and provenance before trusting install instructions.

  • Source link availableRequired

    Open the canonical repository and verify ownership.

    Done
  • Source provenance statusRequired

    Marked as source-backed.

    Done
  • Metadata reviewed

    No reviewed flag detected in metadata.

    Pending

Safety and privacy checks

Complete

Validate risk disclosures before installation or API wiring.

  • Safety notes presentRequired

    Review the listed safety guidance before running commands.

    Done
  • Privacy notes presentRequired

    Review data handling notes before connecting accounts or secrets.

    Done
  • Trust level risk gateRequired

    Trust level does not block evaluation.

    Done

Package and install checks

Needs review

Check package metadata and artifact integrity signals.

  • Install payload available

    Install or copy payload is available for review.

    Done
  • Package verification flag

    No package verification flag provided.

    Pending
  • Checksum metadata

    No checksum provided for downloaded artifact.

    Pending

Compare-driven decision checks

Needs review

Use compare context to validate trade-offs before adoption.

  • Compare tray has multiple entries

    Add at least one more entry to compare trust differences.

    Pending
  • Baseline comparison available

    No baseline peer selected yet.

    Pending
  • Diverging trust signals identified

    No major trust-signal divergence found.

    Pending

Setup at a glance

CLI install

Copy-ready — paste the snippet to get started.

15 minutes

Adoption plan

Balanced adoption plan

Current risk score 24/100. Use staged verification before broader rollout.

Risk 24

Pre-adoption checks

Validate source and review signals before any execution.

  • Confirm source provenanceRequired

    Source URL/provenance metadata is present.

    Done
  • Confirm metadata review state

    No review metadata found; increase manual validation.

    Pending
  • Verify install payload

    Install/config payload exists and can be inspected.

    Done

Security checks

Confirm safety, privacy, and package integrity signals.

  • Review safety notesRequired

    Safety notes are present.

    Done
  • Review privacy notesRequired

    Privacy notes are present.

    Done
  • Verify package integrity metadata

    No package verification/checksum metadata.

    Pending

Rollout

Adopt in controlled steps based on the selected plan.

  • Run in isolated sandbox firstRequired

    Use a constrained sandbox and observe behavior across multiple tasks.

    Pending
  • Roll out graduallyRequired

    Roll out to a small cohort before wider usage.

    Pending
  • Set monitoring and fallback

    Define rollback path and monitor errors after adoption.

    Pending

Evidence readiness

Evidence readiness matrix · balanced

Missing required evidence: Metadata review. Risk score 31.

Risk 31

Source provenance

Present

Source repository/provenance is listed.

Required in this preset

Metadata review

Missing

Review metadata is missing.

Required in this preset

Safety notes

Present

Safety notes are present.

Required in this preset

Privacy notes

Present

Privacy notes are present.

Optional in this preset

Package integrity

Missing

Package integrity metadata is missing.

Optional in this preset

Install payload

Present

Install payload is available.

Required in this preset

Required gaps: Metadata review

Decision timeline

Decision timeline · balanced

Blocking gaps: Check metadata review status. Risk 28.

Risk 28

triage

Confirm source provenanceRequired

Source/provenance metadata is available.

Done

triage

Check metadata review statusRequired

Review metadata is missing.

Pending

verify

Review safety notesRequired

Safety notes are available.

Done

verify

Review privacy notes

Privacy notes are available.

Done

verify

Validate package integrity metadata

Package integrity metadata is missing.

Pending

rollout

Verify install payload and commandsRequired

Install payload is available.

Done

Blockers: Check metadata review status

Prerequisite readiness

Prerequisite readiness

4 prerequisites to line up before setup. Have accounts and credentials ready first. Includes a review or approval gate.

0/4 ready
Account & credentials1Install & runtime1Review & approval1General115 minutes

Safety & privacy surface

Safety & privacy surface

5 safety and 4 privacy notes across 4 risk areas. Review closely: credentials & tokens, network access, third-party handling.

4 areas
  • SafetyGeneralVibe Check MCP is in final maintenance mode according to the project README, so check the repository and package state before adopting it broadly.
  • SafetyGeneralThe server is an advisory oversight layer; it can challenge assumptions but cannot guarantee safe, correct, or complete decisions.
  • SafetyThird-party handlingThe `vibe_check` tool sends the goal, plan, optional user prompt, progress, uncertainties, and task context to a configured LLM provider for critique.
  • SafetyGeneralThe `vibe_learn` tool can record mistakes, preferences, successes, categories, and solutions for later reflection.
  • SafetyCredentials & tokensConstitution tools can help apply session rules, but they do not enforce external system permissions or prevent client-side actions by themselves.
  • PrivacyCredentials & tokensAgent plans, prompts, progress, uncertainties, session identifiers, mistakes, and learned preferences can reveal confidential project details.
  • PrivacyCredentials & tokensProvider API keys are loaded from environment variables and should be scoped, stored, and rotated according to local policy.
  • PrivacyNetwork accessDepending on provider selection, request metadata and prompt content may be processed by Gemini, OpenAI, OpenRouter, Anthropic, or compatible endpoints.
  • PrivacyCredentials & tokensAvoid sending secrets, private code, customer data, or regulated information in `vibe_check` or `vibe_learn` inputs unless approved for the selected provider.

Safety notes

  • Vibe Check MCP is in final maintenance mode according to the project README, so check the repository and package state before adopting it broadly.
  • The server is an advisory oversight layer; it can challenge assumptions but cannot guarantee safe, correct, or complete decisions.
  • The `vibe_check` tool sends the goal, plan, optional user prompt, progress, uncertainties, and task context to a configured LLM provider for critique.
  • The `vibe_learn` tool can record mistakes, preferences, successes, categories, and solutions for later reflection.
  • Constitution tools can help apply session rules, but they do not enforce external system permissions or prevent client-side actions by themselves.

Privacy notes

  • Agent plans, prompts, progress, uncertainties, session identifiers, mistakes, and learned preferences can reveal confidential project details.
  • Provider API keys are loaded from environment variables and should be scoped, stored, and rotated according to local policy.
  • Depending on provider selection, request metadata and prompt content may be processed by Gemini, OpenAI, OpenRouter, Anthropic, or compatible endpoints.
  • Avoid sending secrets, private code, customer data, or regulated information in `vibe_check` or `vibe_learn` inputs unless approved for the selected provider.

Prerequisites

  • Node.js 20 or newer and npx available to the MCP client runtime.
  • At least one supported LLM provider credential, such as Gemini, OpenAI, OpenRouter, or Anthropic.
  • Review of the project's maintenance notice before relying on it for long-lived workflows.
  • Clear policy for what agent plans, prompts, progress notes, and session context may be sent to a second model provider.

Schema details

Install type
cli
Troubleshooting
No
Source repository stats
Scope
Source repo
Collection metadata
Estimated setup
15 minutes
Difficulty
intermediate
Full copyable content
{
  "mcpServers": {
    "vibe-check-mcp": {
      "command": "npx",
      "args": ["-y", "@pv-bhat/vibe-check-mcp", "start", "--stdio"],
      "env": {
        "GEMINI_API_KEY": ""
      }
    }
  }
}

About this resource

Content

Vibe Check MCP is a maintenance-mode Model Context Protocol server for adding mentor-style plan review to agent workflows. It gives Claude tools for asking a second model to critique a goal and plan, recording recurring lessons, and checking session-specific rules before continuing complex work.

The server is designed for moments where an agent may be over-engineering, following an unsupported assumption, or preparing to take an irreversible step. It does not replace human review, tests, access control, or policy enforcement.

Source Review

These sources were reviewed on 2026-06-06. Prefer the live repository, README, npm registry metadata, package metadata, MCP registry manifest, source implementation, and license file for current install commands, maintenance status, supported providers, tool schemas, transport options, and licensing.

Features

  • npm package @pv-bhat/vibe-check-mcp.
  • Stdio MCP server launched with npx -y @pv-bhat/vibe-check-mcp start --stdio.
  • Optional HTTP transport documented upstream for clients that support it.
  • vibe_check tool for goal and plan critique with optional prompt, progress, uncertainty, task context, session ID, and provider override fields.
  • vibe_learn tool for recording mistakes, preferences, successes, categories, and solutions.
  • update_constitution, reset_constitution, and check_constitution tools for session rules.
  • Gemini, OpenAI, OpenRouter, Anthropic, and Anthropic-compatible endpoint support in the source implementation.
  • MIT license.

Installation

Configure the stdio server in your MCP client:

{
  "mcpServers": {
    "vibe-check-mcp": {
      "command": "npx",
      "args": ["-y", "@pv-bhat/vibe-check-mcp", "start", "--stdio"],
      "env": {
        "GEMINI_API_KEY": ""
      }
    }
  }
}

After restarting the MCP client, ask Claude to call vibe_check before a high-risk, ambiguous, or irreversible plan, then use the critique to revise the next step.

Use Cases

  • Challenge a coding agent's plan before a large refactor.
  • Ask for an independent critique before running destructive commands.
  • Record recurring mistakes and fixes with vibe_learn.
  • Attach temporary session rules to a long agent workflow.
  • Add a lightweight reflection step before ambiguous product or architecture work.
  • Compare a proposed plan against uncertainties and task context.

Safety and Privacy

Vibe Check MCP should be treated as advisory. A second model can provide useful critique, but it can also misunderstand context, miss risks, or produce overconfident guidance. Continue to rely on tests, review, access controls, and human judgment for important changes.

Inputs to vibe_check and vibe_learn may include private plans, prompts, progress notes, code context, lessons learned, and session identifiers. Those values can be sent to the configured model provider, so keep secrets, customer data, regulated information, and unreleased project details out of requests unless that provider and environment are approved.

Duplicate Check

Existing MCP content includes planning, coding, browser, and automation servers, but no dedicated entry for PV-Bhat/vibe-check-mcp-server or the @pv-bhat/vibe-check-mcp package was found in content/mcp. This entry is distinct because it covers metacognitive agent oversight tools rather than a general coding, browser, or task-management MCP server.

Source citations

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How it compares

Vibe Check MCP side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.

Field

Maintenance-mode MCP server that adds metacognitive oversight tools for challenging an agent's plan, recording recurring mistakes, and applying session rules before complex or high-risk work continues.

Open dossier

MCP server and CLI for AI image and video generation with gallery search, prompt enhancement, model listing, local preferences, ComfyUI workflows, MeiGen Cloud, and OpenAI-compatible provider support.

Open dossier

MCP server that turns an AI coding agent's plan into a local interactive flowchart, approval workflow, execution tracker, branch selector, and plan history before code changes proceed.

Open dossier

MCP server for AI-assisted development task planning, decomposition, dependency tracking, execution workflow state, reflection, and research mode.

Open dossier
Next steps
Trust
Review statusNot reviewedNot reviewedNot reviewedNot reviewed
Package trustPackage not verifiedPackage not verifiedPackage not verifiedPackage not verified
Source provenanceSource-backedSource-backedSource-backedSource-backed
Submitteroktofeesh1oktofeesh1oktofeesh1oktofeesh1
Install riskReview firstReview firstReview firstReview first
Notes Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓
BrandMeiGen AI Design MCP logoMeiGen AI Design MCP
Categorymcpmcpmcpmcp
SourceSource-backedSource-backedSource-backedSource-backed
AuthorPV BhatMeiGenSixthcjo4m06
Added2026-06-062026-06-062026-06-062026-06-05
Platforms
Harness
Source repo
Safety notesVibe Check MCP is in final maintenance mode according to the project README, so check the repository and package state before adopting it broadly. The server is an advisory oversight layer; it can challenge assumptions but cannot guarantee safe, correct, or complete decisions. The `vibe_check` tool sends the goal, plan, optional user prompt, progress, uncertainties, and task context to a configured LLM provider for critique. The `vibe_learn` tool can record mistakes, preferences, successes, categories, and solutions for later reflection. Constitution tools can help apply session rules, but they do not enforce external system permissions or prevent client-side actions by themselves.MeiGen can submit image and video generation jobs to external providers or a local ComfyUI backend. Image and video generation may spend credits, use paid APIs, or consume local GPU resources depending on the configured provider. Local reference images can be compressed and uploaded through the configured upload gateway before being sent to API providers. Video generation can take minutes, may time out at the MCP client, and should not be retried blindly because jobs or credits may already be in progress. ComfyUI workflow import and modification can run local workflows; review workflow JSON and custom nodes before use.Overture is an approval and visualization layer; it does not replace code review, tests, access controls, or human judgment. The server starts a local web UI and WebSocket service for plan review and execution tracking. Plan approval, pause, rerun, branch selection, and node-status tools affect what the agent is instructed to do next, but they do not sandbox the agent's other tools. File attachments and node metadata can influence later agent steps, so review attached context and secret fields before approval. If local UI ports are exposed beyond trusted local clients, plan data and attachments could be visible to unintended users.Shrimp Task Manager persists task plans, project rules, research notes, task status, dependencies, and execution history in `DATA_DIR`. Tools such as `execute_task`, `complete_task`, `delete_task`, `clear_all_tasks`, and `update_task` can change workflow state and should be reviewed before treating status as authoritative. Agent-assignment and planning features can make generated work look more complete than it is; require human review before accepting implementation progress. The optional GUI starts a web interface when enabled; keep it local or protect it with appropriate network controls. Avoid launching development agents with broad permission-bypass flags solely because an example command suggests it.
Privacy notesAgent plans, prompts, progress, uncertainties, session identifiers, mistakes, and learned preferences can reveal confidential project details. Provider API keys are loaded from environment variables and should be scoped, stored, and rotated according to local policy. Depending on provider selection, request metadata and prompt content may be processed by Gemini, OpenAI, OpenRouter, Anthropic, or compatible endpoints. Avoid sending secrets, private code, customer data, or regulated information in `vibe_check` or `vibe_learn` inputs unless approved for the selected provider.API tokens, OpenAI-compatible credentials, provider base URLs, upload gateway URLs, ComfyUI URLs, preferences, recent generations, prompts, model IDs, and output paths can reveal private creative workflows. Uploaded reference images may become public URLs through the configured upload gateway before generation. Prompts, reference images, generated image URLs, generated video URLs, saved outputs, and favorite prompts can contain client work, brand plans, product images, personal likenesses, or unreleased campaigns. Keep secrets in MCP client environment configuration, review generated media before sharing, and remove local outputs when retention is no longer needed.Plans can include workspace paths, task descriptions, risks, expected outputs, user inputs, selected branches, node outputs, and agent execution status. Overture stores project history in a project `.overture.json` file when possible and falls back to local user-level storage. Uploaded attachments are saved under local Overture attachment storage and can include code, documents, images, or secrets if the user provides them. The UI includes file-reading and attachment endpoints for local plan review, so use it only with trusted local clients and approved workspaces. The marketplace panel can fetch remote MCP marketplace data, so network policy should account for that optional UI feature.Task descriptions, project rules, research notes, dependency graphs, agent assignments, prompts, and execution history may include private implementation details. Persisted task data and backups can reveal roadmap items, security work, customer requests, or internal architecture decisions. Tool arguments and task records may be visible to the MCP client and model provider during planning and execution. Review `DATA_DIR` before committing, sharing, archiving, or uploading generated task state.
Prerequisites
  • Node.js 20 or newer and npx available to the MCP client runtime.
  • At least one supported LLM provider credential, such as Gemini, OpenAI, OpenRouter, or Anthropic.
  • Review of the project's maintenance notice before relying on it for long-lived workflows.
  • Clear policy for what agent plans, prompts, progress notes, and session context may be sent to a second model provider.
  • Node.js 18 or newer.
  • MCP client that can launch a local Node stdio server.
  • Optional MeiGen API token, OpenAI-compatible API key, or local ComfyUI workflow configuration for generation tools.
  • Provider pricing, credits, model limits, and content policy reviewed before image or video generation.
  • Node.js 18 or newer and npx available to the MCP client runtime.
  • A coding-agent workflow that will call Overture planning tools before making changes.
  • Review of local browser, file attachment, and plan-history storage behavior before using it with private workspaces.
  • Node.js 18 or newer and npm available to the MCP client runtime.
  • A cloned, installed, and built checkout of `mcp-shrimp-task-manager`, or an installed package that exposes the built server.
  • A writable `DATA_DIR` for persistent task data and backups.
  • Agreement on how generated plans, task status, agent assignments, and research notes should be reviewed.
Install
npx -y @pv-bhat/vibe-check-mcp start --stdio
Run `npx -y meigen` from an MCP client, or use the upstream plugin/init commands after reviewing provider credentials and generated-media costs.
npx -y overture-mcp
npm install -g mcp-shrimp-task-manager
Config
{
  "mcpServers": {
    "vibe-check-mcp": {
      "command": "npx",
      "args": ["-y", "@pv-bhat/vibe-check-mcp", "start", "--stdio"],
      "env": {
        "GEMINI_API_KEY": "",
        "OPENAI_API_KEY": "",
        "OPENROUTER_API_KEY": "",
        "ANTHROPIC_API_KEY": "",
        "DEFAULT_LLM_PROVIDER": "gemini"
      }
    }
  }
}
{
  "mcpServers": {
    "meigen": {
      "command": "npx",
      "args": ["-y", "meigen"],
      "env": {
        "MEIGEN_API_TOKEN": "{meigen-api-token}"
      }
    }
  }
}
{
  "mcpServers": {
    "overture": {
      "command": "npx",
      "args": ["-y", "overture-mcp"],
      "env": {
        "OVERTURE_AUTO_OPEN": "false",
        "OVERTURE_HTTP_PORT": "3031",
        "OVERTURE_WS_PORT": "3030"
      }
    }
  }
}
{
  "mcpServers": {
    "shrimp-task-manager": {
      "command": "node",
      "args": ["PATH_TO_SHRIMP_TASK_MANAGER/dist/index.js"],
      "env": {
        "DATA_DIR": "WRITABLE_SHRIMP_DATA_DIR",
        "TEMPLATES_USE": "en",
        "ENABLE_GUI": "false"
      }
    }
  }
}
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