Skip to main content
Glama

Audiense Insights MCP Server

Official
by AudienseCo

🏆 Audiense Insights MCP Server

This server, based on the Model Context Protocol (MCP), allows Claude or any other MCP-compatible client to interact with your Audiense Insights account. It extracts marketing insights and audience analysis from Audiense reports, covering demographic, cultural, influencer, and content engagement analysis.

🚀 Prerequisites

Before using this server, ensure you have:

  • Node.js (v18 or higher)
  • Claude Desktop App
  • Audiense Insights Account with API credentials
  • X/Twitter API Bearer Token (optional, for enriched influencer data)

⚙️ Configuring Claude Desktop

  1. Open the configuration file for Claude Desktop:
    • MacOS:
      code ~/Library/Application\ Support/Claude/claude_desktop_config.json
    • Windows:
      code %AppData%\Claude\claude_desktop_config.json
  2. Add or update the following configuration:
    "mcpServers": { "audiense-insights": { "command": "npx", "args": [ "-y", "mcp-audiense-insights" ], "env": { "AUDIENSE_CLIENT_ID": "your_client_id_here", "AUDIENSE_CLIENT_SECRET": "your_client_secret_here", "TWITTER_BEARER_TOKEN": "your_token_here" } } }
  3. Save the file and restart Claude Desktop.

🛠️ Available Tools

📌 get-reports

Description: Retrieves the list of Audiense insights reports owned by the authenticated user.

  • Parameters: None
  • Response:
    • List of reports in JSON format.

📌 get-report-info

Description: Fetches detailed information about a specific intelligence report, including:

  • Status
  • Segmentation type
  • Audience size
  • Segments
  • Access links
  • Parameters:
    • report_id (string): The ID of the intelligence report.
  • Response:
    • Full report details in JSON format.
    • If the report is still processing, returns a message indicating the pending status.

📌 get-audience-insights

Description: Retrieves aggregated insights for a given audience, including:

  • Demographics: Gender, age, country.
  • Behavioral traits: Active hours, platform usage.
  • Psychographics: Personality traits, interests.
  • Socioeconomic factors: Income, education status.
  • Parameters:
    • audience_insights_id (string): The ID of the audience insights.
    • insights (array of strings, optional): List of specific insight names to filter.
  • Response:
    • Insights formatted as a structured text list.

📌 get-baselines

Description: Retrieves available baseline audiences, optionally filtered by country.

  • Parameters:
    • country (string, optional): ISO country code to filter by.
  • Response:
    • List of baseline audiences in JSON format.

📌 get-categories

Description: Retrieves the list of available affinity categories that can be used in influencer comparisons.

  • Parameters: None
  • Response:
    • List of categories in JSON format.

📌 compare-audience-influencers

Description: Compares influencers of a given audience with a baseline audience. The baseline is determined as follows:

  • If a single country represents more than 50% of the audience, that country is used as the baseline.
  • Otherwise, the global baseline is used.
  • If a specific segment is selected, the full audience is used as the baseline.

Each influencer comparison includes:

  • Affinity (%) – How well the influencer aligns with the audience.
  • Baseline Affinity (%) – The influencer’s affinity within the baseline audience.
  • Uniqueness Score – How distinct the influencer is compared to the baseline.
  • Parameters:
    • audience_influencers_id (string): ID of the audience influencers.
    • baseline_audience_influencers_id (string): ID of the baseline audience influencers.
    • cursor (number, optional): Pagination cursor.
    • count (number, optional): Number of items per page (default: 200).
    • bio_keyword (string, optional): Filter influencers by bio keyword.
    • entity_type (enum: person | brand, optional): Filter by entity type.
    • followers_min (number, optional): Minimum number of followers.
    • followers_max (number, optional): Maximum number of followers.
    • categories (array of strings, optional): Filter influencers by categories.
    • countries (array of strings, optional): Filter influencers by country ISO codes.
  • Response:
    • List of influencers with affinity scores, baseline comparison, and uniqueness scores in JSON format.

📌 get-audience-content

Description: Retrieves audience content engagement details, including:

  • Liked Content: Most popular posts, domains, emojis, hashtags, links, media, and a word cloud.
  • Shared Content: Most shared content categorized similarly.
  • Influential Content: Content from influential accounts.

Each category contains:

  • popularPost: Most engaged posts.
  • topDomains: Most mentioned domains.
  • topEmojis: Most used emojis.
  • topHashtags: Most used hashtags.
  • topLinks: Most shared links.
  • topMedia: Shared media.
  • wordcloud: Most frequently used words.
  • Parameters:
    • audience_content_id (string): The ID of the audience content.
  • Response:
    • Content engagement data in JSON format.

📌 report-summary

Description: Generates a comprehensive summary of an Audiense report, including:

  • Report metadata (title, segmentation type)
  • Full audience size
  • Detailed segment information
  • Top insights for each segment (bio keywords, demographics, interests)
  • Top influencers for each segment with comparison metrics
  • Parameters:
    • report_id (string): The ID of the intelligence report to summarize.
  • Response:
    • Complete report summary in JSON format with structured data for each segment
    • For pending reports: Status message indicating the report is still processing
    • For reports without segments: Message indicating there are no segments to analyze

💡 Predefined Prompts

This server includes a preconfigured prompts

  • audiense-demo: Helps analyze Audiense reports interactively.
  • segment-matching: A prompt to match and compare audience segments across Audiense reports, identifying similarities, unique traits, and key insights based on demographics, interests, influencers, and engagement patterns.

Usage:

  • Accepts a reportName argument to find the most relevant report.
  • If an ID is provided, it searches by report ID instead.

Use case: Structured guidance for audience analysis.

🛠️ Troubleshooting

Tools Not Appearing in Claude

  1. Check Claude Desktop logs:
tail -f ~/Library/Logs/Claude/mcp*.log
  1. Verify environment variables are set correctly.
  2. Ensure the absolute path to index.js is correct.

Authentication Issues

  • Double-check OAuth credentials.
  • Ensure the refresh token is still valid.
  • Verify that the required API scopes are enabled.

📜 Viewing Logs

To check server logs:

For MacOS/Linux:

tail -n 20 -f ~/Library/Logs/Claude/mcp*.log

For Windows:

Get-Content -Path "$env:AppData\Claude\Logs\mcp*.log" -Wait -Tail 20

🔐 Security Considerations

  • Keep API credentials secure – never expose them in public repositories.
  • Use environment variables to manage sensitive data.

📄 License

This project is licensed under the Apache 2.0 License. See the LICENSE file for more details.

Related MCP Servers

  • A
    security
    A
    license
    A
    quality
    Enables users to access and manage Replicate's AI models and predictions via the Model Context Protocol, offering tools for creating, canceling, and retrieving model predictions and parameters.
    Last updated -
    138
    83
    MIT License
  • -
    security
    F
    license
    -
    quality
    Enables interaction with the Audius music platform API, supporting user, track, and playlist operations through the Model Context Protocol.
    Last updated -
    11
    • Apple
  • -
    security
    A
    license
    -
    quality
    Allows Claude and other MCP-compatible AI models to access TweetBinder by Audiense analytics data, enabling analysis of hashtags, users, and conversations on Twitter/X with engagement metrics, sentiment analysis, and report generation.
    Last updated -
    1
    Apache 2.0
    • Apple
  • A
    security
    A
    license
    A
    quality
    Enables AI assistants to interact with LinkedIn data through the Model Context Protocol, allowing profile searches, job discovery, messaging, and network analytics.
    Last updated -
    28
    40
    15
    MIT License
    • Apple

View all related MCP servers

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/AudienseCo/mcp-audiense-insights'

If you have feedback or need assistance with the MCP directory API, please join our Discord server