mcp-audiense-intelligence-extractor

🛑 Notice: System Status Update

🚫 This utility is officially deprecated and is no longer under active maintenance.

The Audiense Insights MCP functionality has been transitioned to a service-side, remote architecture. To integrate with the contemporary version, please direct your inquiries to support@audiense.com for guidance.

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⭐ Audiense Intelligence MCP Gateway Service

This dedicated server adheres to the Model Context Protocol (MCP) specification, enabling secure communication between Claude or any compliant MCP client and your established Audiense Insights environment. Its core function is to procure sophisticated marketing intelligence and comprehensive audience segmentation analysis derived from Audiense's proprietary reports, encompassing analysis of cultural nuances, key opinion leaders, content interaction patterns, and audience demographics.

▶️ Initial Setup Requirements

Before deployment, confirm the presence of the following components:

• Runtime Environment: Node.js (version 18 or newer recommended)
• Client Application: Claude Desktop Software
• Audiense Access: A valid Audiense Insights Account furnished with necessary API credentials
• Optional Data Source: X/Twitter API Bearer Token (required for enriched influencer profiling)

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💻 Configuring the Claude Desktop Environment

1. Access the configuration file designated for Claude Desktop:

2. macOS Path: bash code ~/Library/Application\ Support/Claude/claude_desktop_config.json

3. Windows Path: bash code %AppData%\Claude\claude_desktop_config.json

4. Integrate or modify the following configuration block within the JSON structure:

"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" }
}
}

1. Save the configuration file and initiate a restart of the Claude Desktop application.

⚙️ Available Operational Tools

📌 get-reports

Function: Retrieves the registry of Audiense intelligence reports associated with the authenticated user profile.

• Inputs: None Required
• Output: A JSON array containing the discovered report metadata.

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📌 get-report-info

Function: Fetches the granular specifics of a designated intelligence artifact, detailing: - Processing status - Segmentation methodology employed - Total audience size metrics - Constituent segments breakdown - Access URLs

• Inputs:

• report_id (String): The unique identifier for the intelligence artifact.

• Output:

• Detailed report specifications in JSON format.
• Status message if the report is currently undergoing processing.

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📌 get-audience-insights

Function: Queries quantified analytical data for a specified audience set, encompassing: - Demographic Vectors: Age distribution, gender split, geographic location. - Engagement Patterns: Peak activity hours, platform adoption ratios. - Psychographic Profiles: Core personality attributes, topic affinities. - Socioeconomic Indicators: Education levels, perceived income brackets.

• Inputs:
• audience_insights_id (String): The unique identifier for the audience analysis payload.

• insights (Array of Strings, Optional): A selector list to narrow the requested analytical dimensions.

• Output:

• Insights presented as a structured, enumerated text listing.

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📌 get-baselines

Function: Fetches the catalog of accessible reference baseline audiences, optionally allowing filtering based on nation or territory.

• Inputs:

• country (String, Optional): The ISO code defining the geographical filter.

• Output: A JSON collection detailing the available baseline audience profiles.

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📌 get-categories

Function: Retrieves the comprehensive list of defined affinity classifications usable for benchmarking against influencer profiles.

• Inputs: None Required
• Output: A JSON array enumerating all recognized affinity categories.

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📌 compare-audience-influencers

Function: Executes a comparative analysis between the influencers native to a target audience and those within a predefined baseline audience. The baseline is contextually determined: - If one nation constitutes >50% of the target audience, that nation's data serves as the baseline. - Otherwise, the universal global reference is employed. - If a specific segment is prioritized, the entire target audience becomes the reference.

Each influencer comparison yields: - Affinity Percentage (%) – Measure of alignment with the target audience. - Baseline Affinity (%) – Alignment score against the reference audience. - Uniqueness Index – A metric quantifying how distinct the influencer is relative to the reference group.

• Inputs:
• audience_influencers_id (String): ID for the target audience influencer data set.
• baseline_audience_influencers_id (String): ID for the reference audience influencer data set.
• cursor (Number, Optional): Pagination marker for sequential fetching.
• count (Number, Optional): Desired batch size (default limit is 200).
• bio_keyword (String, Optional): Textual filter applied against influencer biographical descriptions.
• entity_type (Enum: person | brand, Optional): Filter by entity categorization.
• followers_min (Number, Optional): Minimum follower count threshold.
• followers_max (Number, Optional): Maximum follower count threshold.
• categories (Array of Strings, Optional): Filter based on affinity classification IDs.

• countries (Array of Strings, Optional): Filter influencers by specified national ISO codes.

• Output:

• A JSON list of matched influencers, enriched with alignment statistics and uniqueness scores.

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📌 get-audience-content

Function: Retrieves granular metrics concerning audience content consumption and interaction patterns, including data on: - Consumed Media: Top engaging posts, prevalent website domains, frequently utilized emojis, primary hashtags, most linked URLs, media format preferences, and a content keyword cloud. - Distributed Media: High-velocity shared artifacts. - Authoritative Content: Material originating from recognized influential profiles.

Each data group includes: - popularPost: Top-performing content items. - topDomains: Most frequently referenced web destinations. - topEmojis: Prevalent symbolic characters in engagement. - topHashtags: Dominant topical markers. - topLinks: Frequently shared external pointers. - topMedia: Preferred media types shared. - wordcloud: Frequency map of significant textual elements.

• Inputs:

• audience_content_id (String): The unique identifier for the audience content analysis set.

• Output:

• Content engagement statistics formatted as a JSON object.

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📌 report-summary

Function: Synthesizes a holistic overview of an entire Audiense intelligence report, consolidating: - Report identifier details (title, analysis model) - Total quantifiable audience population - Detailed segmentation structure - Key segment findings (e.g., top bio keywords, characteristic demographics, principal interests) - Leading influencers identified per segment, along with their comparative metrics

• Inputs:

• report_id (String): The unique reference for the intelligence report targeted for synthesis.

• Output:

• A complete summary report rendered in structured JSON.
• Status notification if the source report is incomplete.
• Notification if the report lacks defined analytical segments.

💡 Pre-configured Operational Directives

This gateway service incorporates ready-to-use analytical commands: - audiense-demo: Designed to facilitate interactive exploration and analysis of retrieved Audiense data sets. - segment-matching: A directive optimized for cross-report comparison of audience slices, pinpointing overlaps, unique attributes, and core distinguishing insights across demographic, interest, influencer, and engagement dimensions.

Execution Guidance: - Accepts a reportName argument to initiate a search for the most pertinent associated intelligence report. - If a specific ID is supplied, the search priority shifts to that identifier.

Intended Utility: Providing structured, guided workflows for advanced audience diagnostics.

🆘 Operational Debugging & Error Resolution

Tools Not Visible Within the Claude Interface

1. Inspect the Claude Desktop diagnostic output:

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

1. Validate the integrity and correct setting of all environmental variables.
2. Confirm the invocation path for the main execution script (index.js) is accurate.

Connection and Credentials Failures

• Scrutinize the OAuth credential pairs for accuracy.
• Verify the validity period and status of any required refresh tokens.
• Confirm that all necessary API permission scopes have been explicitly granted.

📜 Accessing System Diagnostic Streams

To monitor the real-time activity of the server process:

For MacOS/Linux Clients:

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

For Windows Clients:

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

🛡️ Data Security Protocols

• Mandate stringent protection for all API authentication credentials; avoid all instances of public code exposure.
• Utilize the operating system's environment variable mechanisms for secure management of sensitive configuration data.

📄 Legal Framework

This software is distributed under the terms of the Apache 2.0 License. Detailed stipulations are available within the LICENSE file.

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