Darbot Deepmind MCP Server
The Darbot Deepmind MCP Server provides advanced AI reasoning capabilities through a structured, adaptive thinking framework, along with Azure AD authentication.
Reasoning & Problem-Solving
Break down complex problems into manageable, sequential thought steps with a dynamic reasoning chain
Revise and refine previous thoughts as new insights emerge (via
isRevisionandrevisesThoughtparameters)Branch into multiple reasoning paths to explore alternative solutions (via
branchFromThoughtandbranchId)Adaptively adjust reasoning depth as problem complexity becomes clearer
Generate and verify solution hypotheses throughout the reasoning process
Filter irrelevant information and maintain focus on key aspects at each step
Express uncertainty and backtrack without being constrained to linear reasoning
Authentication & Integrations
Authenticate with Azure Active Directory (AAD) using
microsoft-authentication-cli, supporting interactive, device-code, and silent modesIntegrate with GitHub Copilot extensions via
@github/copilot-sdk
Use Cases: Software architecture design, code review, debugging, research planning, decision-making, project planning, and Azure integration scenarios such as API security and cloud automation.
Provides integration with GitHub Copilot extensions via the GitHub Copilot SDK, enabling advanced AI reasoning and adaptive planning within the Copilot ecosystem.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Darbot Deepmind MCP Serverdesign a scalable microservices architecture for an e-commerce platform"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Darbot Deepmind MCP Server
An MCP server implementation that provides advanced AI reasoning capabilities through Darbot's deepmind thinking framework. This server allows for sophisticated, step-by-step reasoning and problem-solving, making it ideal for complex tasks that require dynamic thought processes and adaptive planning.
Features
Advanced Deepmind Reasoning: Break down complex problems into manageable thought steps
Dynamic Thought Revision: Revise and refine previous thoughts as new insights emerge
Multi-path Reasoning: Support for branching logic and exploring alternative solutions
Adaptive Planning: Dynamically adjust the number of thoughts needed as problem complexity becomes clearer
Solution Hypothesis Generation: Create and verify hypotheses throughout the reasoning process
Context-aware Analysis: Filter irrelevant information while maintaining focus on key aspects
Microsoft Authentication: Seamless Azure AD authentication using microsoft-authentication-cli
GitHub Copilot SDK: Integration with GitHub Copilot extensions via
@github/copilot-sdkBeautiful Console Output: Formatted thought display with colors and borders for enhanced readability
Related MCP server: Visum Thinker MCP Server
Installation
Prerequisites
Node.js 20.19+ or Docker
npm or yarn package manager (for local installation)
Quick Installation
Using NPX (Recommended)
Add this to your claude_desktop_config.json:
{
"mcpServers": {
"darbot-deepmind": {
"command": "npx",
"args": [
"-y",
"@darbotlabs/darbot-deepmind-mcp"
]
}
}
}Using Docker
{
"mcpServers": {
"darbot-deepmind": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"mcp/darbot-deepmind"
]
}
}
}Local Development Installation
# Clone the repository
git clone https://github.com/darbotlabs/darbot-deepmind-mcp.git
cd darbot-deepmind-mcp
# Install dependencies
npm install
# Build the project
npm run build
# Run the server
npm startTool Reference
darbot_deepmind
Facilitates sophisticated, step-by-step reasoning through Darbot's deepmind thinking framework for complex problem-solving and analysis.
Parameters
Parameter | Type | Required | Description |
| string | Yes | The current thinking step |
| boolean | Yes | Whether another thought step is needed |
| integer | Yes | Current thought number (starting from 1) |
| integer | Yes | Estimated total thoughts needed |
| boolean | No | Whether this revises previous thinking |
| integer | No | Which thought number is being reconsidered |
| integer | No | Branching point thought number |
| string | No | Branch identifier for multi-path reasoning |
| boolean | No | If more thoughts are needed beyond initial estimate |
microsoft_auth
Authenticates with Azure Active Directory (AAD) and obtains access tokens using the microsoft-authentication-cli tool.
Prerequisites
azureauthCLI must be installed on your systemAzure AD application must be properly configured with redirect URIs
Client ID, Resource ID, and Tenant ID must be available
Parameters
Parameter | Type | Required | Description |
| string | No* | Azure AD application (client) ID |
| string | No* | Resource ID to authenticate to |
| string | No* | Azure AD tenant ID |
| enum | No | Output format: |
| number | No | Timeout in minutes (default: 15) |
| enum | No | Authentication mode: |
| string | No* | Config alias name (requires |
* Either provide clientId, resourceId, and tenantId OR provide alias
Installing azureauth CLI
Windows:
$env:AZUREAUTH_VERSION = '0.9.2'
$script = "${env:TEMP}\install.ps1"
$url = "https://raw.githubusercontent.com/AzureAD/microsoft-authentication-cli/${env:AZUREAUTH_VERSION}/install/install.ps1"
[Net.ServicePointManager]::SecurityProtocol = [Net.SecurityProtocolType]::Tls12
Invoke-WebRequest $url -OutFile $script; if ($?) { &$script }; if ($?) { rm $script }macOS:
export AZUREAUTH_VERSION='0.9.2'
curl -sL https://raw.githubusercontent.com/AzureAD/microsoft-authentication-cli/$AZUREAUTH_VERSION/install/install.sh | shFor the latest version, check the releases page.
Usage Examples
Basic Problem Solving
The darbot_deepmind tool excels at breaking down complex problems:
// Example: Designing a microservices architecture
{
"thought": "Analyzing requirements for e-commerce platform supporting 1M daily users",
"nextThoughtNeeded": true,
"thoughtNumber": 1,
"totalThoughts": 8
}Thought Revision
When new insights emerge, you can revise previous thoughts:
{
"thought": "Revising inventory service design to include real-time updates via event streaming",
"nextThoughtNeeded": true,
"thoughtNumber": 5,
"totalThoughts": 10,
"isRevision": true,
"revisesThought": 4
}Branching Logic
Explore alternative solutions:
{
"thought": "Exploring serverless architecture as alternative to containerized microservices",
"nextThoughtNeeded": true,
"thoughtNumber": 6,
"totalThoughts": 12,
"branchFromThought": 3,
"branchId": "serverless-alternative"
}Microsoft Authentication
Authenticate with Azure AD to access protected resources:
// Interactive authentication (opens browser)
{
"clientId": "73e5793e-8f71-4da2-9f71-575cb3019b37",
"resourceId": "67eeda51-3891-4101-a0e3-bf0c64047157",
"tenantId": "a3be859b-7f9a-4955-98ed-f3602dbd954c",
"output": "json",
"mode": "interactive"
}
// Device code flow (for headless environments)
{
"clientId": "73e5793e-8f71-4da2-9f71-575cb3019b37",
"resourceId": "67eeda51-3891-4101-a0e3-bf0c64047157",
"tenantId": "a3be859b-7f9a-4955-98ed-f3602dbd954c",
"output": "json",
"mode": "device-code",
"timeout": 20
}
// Using config alias (requires AZUREAUTH_CONFIG environment variable)
{
"alias": "production-app",
"output": "token",
"mode": "silent"
}Configuration
Environment Variables
Variable | Default | Description |
|
| Set to |
|
| Port for MCP server (when running standalone) |
|
| Logging level: |
| - | Path to azureauth config file (for using aliases) |
| - | Application Insights ingestion token (enables telemetry for azureauth) |
VS Code Integration
For VS Code users, you can install via:
Or manually add to .vscode/mcp.json:
{
"servers": {
"darbot-deepmind": {
"command": "npx",
"args": [
"-y",
"@darbotlabs/darbot-deepmind-mcp"
]
}
}
}Building from Source
Docker Build
# Build the Docker image
docker build -t mcp/darbot-deepmind -f Dockerfile .
# Run the Docker container
docker run --rm -i mcp/darbot-deepmindLocal Build
# Install dependencies
npm install
# Run tests
npm test
# Build TypeScript
npm run build
# Start the server
npm startTroubleshooting
Common Issues and Solutions
1. Server Not Starting
Symptom: The server fails to start or immediately exits.
Solutions:
Ensure Node.js 20.19+ is installed:
node --versionCheck if port 3000 is available:
netstat -an | findstr 3000(Windows) orlsof -i :3000(macOS/Linux)Verify all dependencies are installed:
npm installCheck for TypeScript compilation errors:
npm run buildClear npm cache:
npm cache clean --force
2. Tool Not Discovered
Symptom: Claude or VS Code shows "0 tools discovered".
Solutions:
Verify the server is running: Check process list with
tasklist | findstr node(Windows) orps aux | grep node(macOS/Linux)Ensure MCP protocol version compatibility
Check server logs for registration errors
Restart Claude Desktop or VS Code after configuration changes
Verify JSON syntax in configuration files
3. Thought Logging Issues
Symptom: Too much or no thought logging in console.
Solutions:
To disable logging: Set
DISABLE_THOUGHT_LOGGING=trueTo enable debug logging: Set
LOG_LEVEL=debugCheck log file permissions in Docker containers
Verify environment variables are properly set
4. Docker Container Issues
Symptom: Docker container exits immediately or fails to respond.
Solutions:
Ensure Docker daemon is running:
docker infoCheck container logs:
docker logs <container-id>Verify the image was built successfully:
docker imagesTry running with
-itflags for interactive mode:docker run -it --rm mcp/darbot-deepmindCheck Docker memory and CPU limits
5. NPX Installation Failures
Symptom: NPX command fails or hangs.
Solutions:
Clear npm cache:
npm cache clean --forceTry with explicit registry:
npx --registry https://registry.npmjs.org/ -y @darbotlabs/darbot-deepmind-mcpCheck network proxy settings
Use local installation method instead
Verify npm version:
npm --version(should be 7+)
6. Memory or Performance Issues
Symptom: Server becomes slow or unresponsive with complex problems.
Solutions:
Limit thought depth for very complex problems
Monitor memory usage during operation:
topor Task ManagerConsider breaking very large problems into sub-problems
Adjust Node.js memory limits:
node --max-old-space-size=4096 dist/index.jsCheck for memory leaks in thought history
7. Configuration Not Loading
Symptom: Configuration changes not taking effect.
Solutions:
Verify JSON syntax in configuration files: Use online JSON validator
Check file paths are correct (especially on Windows)
Ensure proper escaping of backslashes in Windows paths: Use double backslashes
\\\\or forward slashes/Restart the MCP client after configuration changes
Check file permissions
8. TypeScript Compilation Errors
Symptom: Build fails with TypeScript errors.
Solutions:
Check TypeScript version compatibility:
npx tsc --versionVerify
tsconfig.jsonconfigurationClear TypeScript build cache:
rm -rf dist && npm run buildCheck for conflicting type definitions
Ensure all dependencies are properly installed
9. Permission Issues (Linux/macOS)
Symptom: Permission denied errors when running scripts.
Solutions:
Make scripts executable:
chmod +x dist/*.jsCheck file ownership:
ls -laRun with appropriate permissions:
sudo npm install -gUse
nvmfor Node.js version management to avoid permission issues
10. Microsoft Authentication Issues
Symptom: Authentication fails or azureauth command not found.
Solutions:
Verify
azureauthis installed: Runazureauth --versionInstall
azureauthif missing: See installation instructionsCheck if
azureauthis in PATH: Runwhich azureauth(macOS/Linux) orwhere azureauth(Windows)For headless Linux, use device code flow: Set
mode: "device-code"Verify Azure AD app registration has correct redirect URIs configured
Check client ID, resource ID, and tenant ID are correct
For timeout issues, increase timeout value:
timeout: 30(in minutes)Clear token cache if getting stale tokens: Delete cache files in
~/.azureauth(macOS/Linux) or%LOCALAPPDATA%\AzureAuth(Windows)Enable debug logging for azureauth: Set environment variable
AZUREAUTH_LOG_LEVEL=debug
Debug Mode
To enable detailed debugging:
# Set environment variables
export LOG_LEVEL=debug
export DISABLE_THOUGHT_LOGGING=false
# Windows PowerShell
$env:LOG_LEVEL="debug"
$env:DISABLE_THOUGHT_LOGGING="false"
# Run with debug output
npm startGetting Help
If you encounter issues not covered here:
Check the GitHub Issues
Enable debug logging and collect logs
Create a minimal reproduction case
File a new issue with:
System information (OS, Node.js version, npm version)
Complete error messages
Steps to reproduce
Configuration files (redacted if necessary)
Debug logs
Use Cases
The Darbot Deepmind MCP server is ideal for:
Software Architecture Design: Breaking down complex system requirements
Problem Analysis: Systematic exploration of multi-faceted issues
Research Planning: Developing comprehensive research strategies
Decision Making: Evaluating options with structured thinking
Code Review: Analyzing code with step-by-step reasoning
Learning and Education: Breaking down complex topics into understandable steps
Project Planning: Decomposing large projects into manageable tasks
Debugging: Systematic approach to identifying and solving issues
Azure Integration: Authenticating with Azure AD for accessing protected resources
API Development: Securing API calls with Azure AD tokens
Cloud Automation: Integrating Azure authentication in automated workflows
Performance Considerations
Memory Usage: Each thought is stored in memory. For very long reasoning chains, consider breaking into smaller sessions
Response Time: Complex formatting may add small delays. Disable logging for production if needed
Concurrency: The server handles one reasoning chain at a time per instance
Contributing
We welcome contributions! Please see our Contributing Guide for details.
Development Setup
# Clone the repository
git clone https://github.com/darbotlabs/darbot-deepmind-mcp.git
cd darbot-deepmind-mcp
# Install dependencies
npm install
# Run in development mode
npm run dev
# Run tests with coverage
npm run test:coverage
# Run linting
npm run lint:fix
# Format code
npm run formatChangelog
See CHANGELOG.md for version history and updates.
License
This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.
Acknowledgments
Built with the Model Context Protocol SDK 1.26.0
GitHub Copilot integration via @github/copilot-sdk
Inspired by advanced AI reasoning techniques
Uses Chalk for beautiful console output
Validation powered by Zod 4.x
Azure AD authentication via microsoft-authentication-cli
Made with ❤️ by Darbot Labs
Available Tools
1 tooldarbot_deepmindA
Darbot Deepmind: A sophisticated tool for dynamic and reflective problem-solving through structured thinking. This tool helps analyze complex problems through an adaptive thinking process that evolves with understanding. Each thought can build on, question, or revise previous insights as understanding deepens.
When to use this tool:
Breaking down complex problems into steps
Planning and design with room for revision
Analysis that might need course correction
Problems where the full scope might not be clear initially
Problems that require a multi-step solution
Tasks that need to maintain context over multiple steps
Situations where irrelevant information needs to be filtered out
Key features:
You can adjust total_thoughts up or down as you progress
You can question or revise previous thoughts
You can add more thoughts even after reaching what seemed like the end
You can express uncertainty and explore alternative approaches
Not every thought needs to build linearly - you can branch or backtrack
Generates a solution hypothesis
Verifies the hypothesis based on the Chain of Thought steps
Repeats the process until satisfied
Provides a correct answer
Parameters explained:
thought: Your current thinking step, which can include:
Regular analytical steps
Revisions of previous thoughts
Questions about previous decisions
Realizations about needing more analysis
Changes in approach
Hypothesis generation
Hypothesis verification
next_thought_needed: True if you need more thinking, even if at what seemed like the end
thought_number: Current number in sequence (can go beyond initial total if needed)
total_thoughts: Current estimate of thoughts needed (can be adjusted up/down)
is_revision: A boolean indicating if this thought revises previous thinking
revises_thought: If is_revision is true, which thought number is being reconsidered
branch_from_thought: If branching, which thought number is the branching point
branch_id: Identifier for the current branch (if any)
needs_more_thoughts: If reaching end but realizing more thoughts needed
You should:
Start with an initial estimate of needed thoughts, but be ready to adjust
Feel free to question or revise previous thoughts
Don't hesitate to add more thoughts if needed, even at the "end"
Express uncertainty when present
Mark thoughts that revise previous thinking or branch into new paths
Ignore information that is irrelevant to the current step
Generate a solution hypothesis when appropriate
Verify the hypothesis based on the Chain of Thought steps
Repeat the process until satisfied with the solution
Provide a single, ideally correct answer as the final output
Only set next_thought_needed to false when truly done and a satisfactory answer is reached
| Name | Required | Description | Default |
|---|---|---|---|
| thought | Yes | Your current thinking step | |
| nextThoughtNeeded | Yes | Whether another thought step is needed | |
| thoughtNumber | Yes | Current thought number | |
| totalThoughts | Yes | Estimated total thoughts needed | |
| isRevision | No | Whether this revises previous thinking | |
| revisesThought | No | Which thought is being reconsidered | |
| branchFromThought | No | Branching point thought number | |
| branchId | No | Branch identifier | |
| needsMoreThoughts | No | If more thoughts are needed |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: the tool allows iterative adjustment of thoughts, supports revisions and branching, generates and verifies hypotheses, repeats until satisfied, and outputs a final answer. It covers process flow, flexibility, and output expectations, though it doesn't mention performance aspects like rate limits or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is excessively long (over 400 words) with redundant sections. It repeats concepts (e.g., revision and branching are mentioned multiple times) and includes verbose lists that could be condensed. While structured with headings, it lacks front-loading of critical information and contains unnecessary elaboration, reducing efficiency for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (9 parameters, no output schema, no annotations), the description is quite complete: it explains the tool's purpose, usage guidelines, behavioral process, and parameter semantics in detail. It covers the iterative nature, hypothesis generation, and final output. However, it doesn't specify the format or content of the 'correct answer' output, leaving a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds significant value with a 'Parameters explained' section that elaborates on each parameter's purpose and usage context (e.g., 'thought' can include revisions, questions, hypotheses; 'next_thought_needed' for adding thoughts even at the end). This provides semantic meaning beyond the schema's basic descriptions, compensating well for the high parameter count.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool is for 'dynamic and reflective problem-solving through structured thinking' and 'helps analyze complex problems through an adaptive thinking process', which provides a general purpose. However, it lacks specificity about what concrete action the tool performs (e.g., does it execute analysis, generate reports, or simulate thinking?) and doesn't distinguish from siblings (though none exist). The description is vague about the actual output or mechanism beyond the thinking process.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes a clear 'When to use this tool' section with 7 specific scenarios (e.g., 'Breaking down complex problems into steps', 'Planning and design with room for revision'), providing explicit guidance on when this tool is appropriate. It also lists 11 'You should' instructions that further clarify usage, though no alternatives are mentioned (but no siblings exist).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'darbot_deepmind' has a clearly defined and distinct purpose for structured problem-solving.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'darbot_deepmind' follows a consistent pattern with no deviations to evaluate.
A single tool is too few for a server described as enabling 'dynamic and reflective problem-solving through structured thinking.' This suggests a complex domain that would benefit from multiple specialized tools (e.g., for different thinking modes or problem types), making the current count insufficient for the apparent scope.
The tool surface is severely incomplete for the domain. While the single tool covers a general problem-solving process, there are obvious gaps such as tools for specific analysis techniques, data handling, or integration with external resources that would be expected in a sophisticated problem-solving server.
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