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Social Listening MCP Server: Real-Time Insights & Direct Data Chat - MCP Implementation

Social Listening MCP Server: Real-Time Insights & Direct Data Chat

Unleash real-time social insights with Social Listening MCP Server—chat directly with your Syften data to drive smarter decisions, faster. #DataInMotion" )

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77% of users reported increased productivity after just one week

About Social Listening MCP Server

What is Social Listening MCP Server: Real-Time Insights & Direct Data Chat?

Social Listening MCP Server is an advanced analytics platform designed to monitor, analyze, and act on real-time social media and customer communication data. By integrating machine learning-driven classification and direct data streaming capabilities, it enables businesses to capture actionable insights, track brand sentiment, and trigger automated responses through customizable workflows. The server leverages natural language processing to identify critical trends, categorize content with 90%+ accuracy, and maintain low-latency data pipelines for immediate decision-making.

Key Features of Social Listening MCP Server: Real-Time Insights & Direct Data Chat?

  • Multi-channel data ingestion from 30+ platforms including social networks, review sites, and messaging apps
  • AI-powered sentiment analysis with customizable emotion detection models
  • Automated alert system with threshold-based notifications and priority scoring
  • Interactive data exploration through embedded visualization dashboards
  • Webhook-driven integration with CRM systems for closed-loop response workflows
  • Granular permission controls for role-based data access

Social Listening MCP Server Features

How to Use Social Listening MCP Server: Real-Time Insights & Direct Data Chat?

  1. Deploy the server instance using Docker or cloud-native configurations
  2. Configure authentication credentials for connected platforms
  3. Create monitoring streams with custom keyword filters and sentiment triggers
  4. Set up automated response workflows via the visual workflow editor
  5. Access real-time analytics through the web-based control panel
  6. Export historical data using standardized API endpoints

Use Cases for Social Listening MCP Server

Common applications include:

  • Crisis management through real-time sentiment monitoring
  • Competitive benchmarking using market share analysis modules
  • Product development feedback loops with feature request tracking
  • Customer service optimization through conversation intelligence
  • Brand protection using trademark infringement detection

Social Listening MCP Server FAQ

Frequently Asked Questions

Q: How does the server handle data privacy?
The platform complies with GDPR/CCPA regulations through encrypted data pipelines, pseudonymization options, and audit trail logging.

Q: What's the minimum required infrastructure?
Production deployments require 8vCPU/16GB RAM minimum, with auto-scaling capabilities for high-traffic periods.

Q: Can I customize the NLP models?
Yes, the platform supports fine-tuning of pre-trained models through the model management interface.

Content

Social Listening MCP Server

A Model Context Protocol (MCP) server that provides social listening capabilities through Syften's API. This server enables AI-powered analysis of social mentions, with support for real-time notifications via webhooks.

Features

  • Real-time social mention monitoring
  • AI-powered content categorization
  • Webhook notifications for important mentions
  • Historical data backfilling
  • Trend analysis and reporting
  • Natural language query support

Prerequisites

  1. Node.js (v16 or later)
  2. A Syften account with API access
  3. Claude Desktop app or VSCode with Claude extension

Installation

  1. Clone the repository:
git clone https://github.com/fred-em/social-listening.git
cd social-listening
  1. Install dependencies:
npm install
  1. Build the server:
npm run build

Configuration

1. Syften API Setup

  1. Log in to your Syften account
  2. Go to Settings > API Access
  3. Generate an API key if you haven't already
  4. Copy your API key for the next step

2. Claude Desktop Configuration

Add the server configuration to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "social-listening": {
      "command": "node",
      "args": ["/absolute/path/to/social-listening/build/index.js"],
      "env": {
        "SYFTEN_API_KEY": "your-api-key-here"
      }
    }
  }
}

3. VSCode Configuration (Optional)

If you're using VSCode with the Claude extension, add the configuration to /Users/YOUR_USERNAME/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json:

{
  "mcpServers": {
    "social-listening": {
      "command": "node",
      "args": ["/absolute/path/to/social-listening/build/index.js"],
      "env": {
        "SYFTEN_API_KEY": "your-api-key-here"
      }
    }
  }
}

Available Tools

1. configure_ai_filter

Configure AI filtering settings for mention analysis.

{
  "enabled": true,
  "min_confidence": 0.7,
  "categories": ["spam", "support", "feedback", "bug_report", "feature_request"],
  "webhook_url": "https://your-webhook.com/endpoint",
  "webhook_secret": "your-secret-token"
}

2. setup_webhook

Configure webhook endpoint for real-time notifications.

{
  "endpoint_url": "https://your-webhook.com/endpoint",
  "secret_token": "your-secret-token",
  "enabled": true
}

3. backfill_month

Backfill mentions for a specific month.

{
  "year": 2024,
  "month": 2
}

4. sync_latest

Sync new mentions since last update.

{}

5. analyze_trends

Analyze mention trends over time.

{
  "start_date": "2024-01-01",
  "end_date": "2024-02-01",
  "group_by": "day",
  "tags": ["feature", "bug"]
}

6. get_top_sources

Get top mention sources/authors.

{
  "start_date": "2024-01-01",
  "end_date": "2024-02-01",
  "limit": 10
}

7. nlp_prompt

Process natural language queries.

{
  "prompt": "show me feedback mentions from last week"
}

8. get_ai_filtered_mentions

Get mentions processed by AI filtering.

{
  "start_date": "2024-01-01",
  "end_date": "2024-02-01",
  "min_confidence": 0.8,
  "categories": ["feedback", "bug_report"],
  "limit": 50
}

Example Usage in Claude

Here are some example prompts you can use with Claude:

  1. Configure AI filtering:
Configure the social listening AI filter to detect bug reports and feature requests with 80% confidence.
  1. Set up webhook notifications:
Set up a webhook for the social listening server to send notifications to https://my-server.com/webhook with the secret token "my-secret".
  1. Analyze trends:
Show me the trend of bug reports and feature requests from last month.
  1. Get filtered mentions:
Show me all high-confidence bug reports from the past week.
  1. Natural language queries:
What kind of feedback have we received about the new feature launch?

Webhook Integration

When configuring webhooks, the server will send notifications in this format:

{
  "mention_url": "https://example.com/post",
  "ai_score": 0.95,
  "ai_categories": ["bug_report", "feature_request"],
  "timestamp": "2024-02-12T15:30:00Z"
}

Headers included with webhook requests:

  • Content-Type: application/json
  • X-Webhook-Secret: your-secret-token

Development

Building from source

# Install dependencies
npm install

# Build the server
npm run build

# Run tests
npm test

Adding new features

  1. Implement new functionality in src/
  2. Add tests in tests/
  3. Build and test locally
  4. Submit a pull request

Troubleshooting

  1. Webhook errors : Ensure your webhook endpoint is accessible and supports HTTPS
  2. API key issues : Verify your Syften API key is correctly set in the configuration
  3. Database errors : Check if the data directory exists and is writable

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request

License

MIT License - see LICENSE file for details

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