Human In The Loop

by boorich

9 stars

Not rated

GitHub

About

Facilitates human-in-the-loop processes for AI workflows, enabling seamless integration of human judgment in tasks like content moderation, complex problem-solving, and ethical AI implementations.

Details

Author: boorich
Repository: boorich/mcp-human-loop
GitHub stars: 9
Categories: Productivity, Design, Developer Tools, AI, Project Management, Infrastructure
Tags: #integration

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below
Plugin Name Human In The Loop

Command (node, npx, python, etc.) npx
Arguments
- Argument 1 -y
- Argument 2 @highlight/mcp-server
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "human in the loop": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "human in the loop": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "human in the loop": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "human in the loop": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "human in the loop": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "human in the loop": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "human in the loop": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "human in the loop": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "human in the loop": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "human in the loop": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "human in the loop": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "human in the loop": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "human in the loop": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "human in the loop": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "human in the loop": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

From the repository

[Usage examples to be added]

`Conversational Quality Monitoring`

Assess the depth and constructiveness of dialogue, detect repetitive or circular conversations, and identify when a conversation lacks meaningful progress.

`Cognitive Load Management`

Evaluate the complexity of tasks or discussions, warn when cognitive demands exceed typical processing capabilities, and suggest breaking down complex topics or taking breaks.

`Learning and Skill Development Tracking`

Monitor the educational potential of conversations, identify when a discussion moves beyond or falls short of a learner's current skill level, and recommend supplementary resources or adjust explanation complexity.

`Emotional Intelligence and Sentiment Analysis`

Detect potential emotional escalation in conversations, identify when a discussion becomes overly emotional or unproductive, and suggest de-escalation strategies or communication adjustments.

`Compliance and Ethical Boundary Monitoring`

Proactively identify conversations approaching ethical boundaries, detect potential violations of predefined communication guidelines, and provide early warnings about sensitive or potentially inappropriate content.

`Multi-Agent Coordination`

In scenarios with multiple AI agents or models, determine when to escalate or hand off tasks between different AI capabilities and optimize task allocation based on specialized skills.

`Resource Allocation and Performance Optimization`

Assess computational complexity of ongoing tasks, predict and manage computational resource requirements, and optimize system performance by intelligently routing or prioritizing tasks.

`Cross-Disciplinary Knowledge Integration`

Detect when a conversation requires expertise from multiple domains, identify knowledge gaps or areas needing interdisciplinary insights, and suggest bringing in additional contextual information or expert perspectives.

`Creativity and Innovation Detection`

Recognize when a conversation is generating novel ideas, identify potential breakthrough thinking or unique problem-solving approaches, and encourage and highlight innovative thought patterns.

`Meta-Cognitive Analysis`

Analyze the reasoning and thought processes within a conversation, detect logical fallacies or cognitive biases, and provide insights into the quality of reasoning and argumentation.

`Contextual Relevance in Research and Information Gathering`

Evaluate the relevance and comprehensiveness of information collection, detect when research is becoming too narrow or too broad, and suggest alternative approaches or additional sources.

`Personalization and Adaptive Communication`

Learn and adapt communication styles based on interaction patterns, detect user preferences and communication effectiveness, and dynamically adjust interaction strategies.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "human in the loop": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Linux

{
    "env": [],
    "args": [
        "-y",
        "@highlight/mcp-server"
    ],
    "command": "npx"
}

Macos

{
    "env": [],
    "args": [
        "-y",
        "@highlight/mcp-server"
    ],
    "command": "npx"
}

Windows

{
    "env": [],
    "args": [
        "/c",
        "npx",
        "-y",
        "@highlight/mcp-server"
    ],
    "command": "cmd"
}

MCP Human Loop Server

A Model Context Protocol server that manages human-agent collaboration through a sequential scoring system.

Core Concept

This server acts as an intelligent middleware that determines when human intervention is necessary in AI agent operations. Instead of treating human involvement as a binary decision, it uses a sequential scoring system that evaluates multiple dimensions of a request before deciding if human input is required.

Scoring System

The server evaluates requests through a series of scoring gates. Each gate represents a specific dimension that might require human intervention. A request only proceeds to human review if it triggers threshold values in any of these dimensions:

1. Complexity Score
- Evaluates if the task is too complex for autonomous agent handling
- Considers factors like number of steps, dependencies, and decision branches
- Example: Multi-step tasks with uncertain outcomes score higher

2. Permission Score
- Assesses if the requested action requires human authorization
- Based on predefined permission levels and action types
- Example: Financial transactions above certain amounts require human approval

3. Risk Score
- Measures potential impact and reversibility of actions
- Considers both direct and indirect consequences
- Example: Actions affecting multiple systems or user data score higher

4. Emotional Intelligence Score
- Determines if the task requires human emotional understanding
- Evaluates context and user state
- Example: User frustration or sensitive situations trigger human involvement

5. Confidence Score
- Reflects the agent's certainty about its proposed action
- Lower confidence triggers human review
- Example: Edge cases or unusual patterns lower confidence

Flow Logic

1. Agent submits request to server
2. Server evaluates scores in sequence
3. If any score exceeds its threshold → Route to human
4. If all scores pass → Allow autonomous agent action
5. Track and log all decisions for system improvement

Benefits

- Efficiency: Only truly necessary cases reach human operators
- Scalability: Easy to add new scoring dimensions
- Tunability: Thresholds can be adjusted based on experience
- Transparency: Clear decision path for each human intervention
- Learning: System improves through tracked outcomes

Future Improvements

- Dynamic threshold adjustment based on outcome tracking
- Machine learning integration for score calculation
- Real-time threshold adjustment based on operator load
- Integration with external risk assessment systems

Installation

[Installation instructions to be added]

Usage

[Usage examples to be added]

Contributing

[Contribution guidelines to be added]

ToDo

Conversational Quality Monitoring

- Assess the depth and constructiveness of dialogue
- Detect repetitive or circular conversations
- Identify when a conversation lacks meaningful progress

Cognitive Load Management

- Evaluate the complexity of tasks or discussions
- Warn when the cognitive demands exceed typical processing capabilities
- Suggest breaking down complex topics or taking breaks

Learning and Skill Development Tracking

- Monitor the educational potential of conversations
- Identify when a discussion moves beyond or falls short of a learner's current skill level
- Recommend supplementary resources or adjust explanation complexity

Emotional Intelligence and Sentiment Analysis

- Detect potential emotional escalation in conversations
- Identify when a discussion becomes overly emotional or unproductive
- Suggest de-escalation strategies or communication adjustments

Compliance and Ethical Boundary Monitoring

- Proactively identify conversations approaching ethical boundaries
- Detect potential violations of predefined communication guidelines
- Provide early warnings about sensitive or potentially inappropriate content

Multi-Agent Coordination

- In scenarios with multiple AI agents or models
- Determine when to escalate or hand off tasks between different AI capabilities
- Optimize task allocation based on specialized skills

Resource Allocation and Performance Optimization

- Assess computational complexity of ongoing tasks
- Predict and manage computational resource requirements
- Optimize system performance by intelligently routing or prioritizing tasks

Cross-Disciplinary Knowledge Integration

- Detect when a conversation requires expertise from multiple domains
- Identify knowledge gaps or areas needing interdisciplinary insights
- Suggest bringing in additional contextual information or expert perspectives

Creativity and Innovation Detection

- Recognize when a conversation is generating novel ideas
- Identify potential breakthrough thinking or unique problem-solving approaches
- Encourage and highlight innovative thought patterns

Meta-Cognitive Analysis

- Analyze the reasoning and thought processes within a conversation
- Detect logical fallacies or cognitive biases
- Provide insights into the quality of reasoning and argumentation

Contextual Relevance in Research and Information Gathering

- Evaluate the relevance and comprehensiveness of information collection
- Detect when research is becoming too narrow or too broad
- Suggest alternative approaches or additional sources

Personalization and Adaptive Communication

- Learn and adapt communication styles based on interaction patterns
- Detect user preferences and communication effectiveness
- Dynamically adjust interaction strategies

No reviews yet — be the first

Use Google, GitHub, or an email account so ratings stay tied to real people.

Email sign in

No reviews posted yet.

About

Details

Jump to

Setting up with Highlight

From the repository

Conversational Quality Monitoring

Cognitive Load Management

Learning and Skill Development Tracking

Emotional Intelligence and Sentiment Analysis

Compliance and Ethical Boundary Monitoring

Multi-Agent Coordination

Resource Allocation and Performance Optimization

Cross-Disciplinary Knowledge Integration

Creativity and Innovation Detection

Meta-Cognitive Analysis

Contextual Relevance in Research and Information Gathering

Personalization and Adaptive Communication

Claude Desktop / Cursor

Linux

Macos

Windows

MCP Human Loop Server

Core Concept

Scoring System

Flow Logic

Benefits

Future Improvements

Installation

Usage

Contributing

ToDo

Install

More like this

Time

Sentry

Github

Git

GitLab

Context 7

`Conversational Quality Monitoring`

`Cognitive Load Management`

`Learning and Skill Development Tracking`

`Emotional Intelligence and Sentiment Analysis`

`Compliance and Ethical Boundary Monitoring`

`Multi-Agent Coordination`

`Resource Allocation and Performance Optimization`

`Cross-Disciplinary Knowledge Integration`

`Creativity and Innovation Detection`

`Meta-Cognitive Analysis`

`Contextual Relevance in Research and Information Gathering`

`Personalization and Adaptive Communication`