{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-audiense-insights","slug":"audiense-insights","name":"Audiense Insights","type":"mcp","url":"https://github.com/AudienseCo/mcp-audiense-insights","page_url":"https://unfragile.ai/audiense-insights","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-audiense-insights__cap_0","uri":"capability://tool.use.integration.demographic.audience.segmentation.via.mcp","name":"demographic-audience-segmentation-via-mcp","description":"Exposes Audiense demographic analysis as MCP tools, allowing Claude and other LLM agents to query audience segments by age, gender, location, and income without direct API calls. Implements MCP resource and tool abstractions that translate natural language queries into structured Audiense API requests, returning parsed demographic distributions and segment profiles.","intents":["Query demographic breakdowns of a Twitter/X audience without writing API code","Build an LLM agent that automatically segments audiences by age and geography for campaign targeting","Integrate audience demographics into a multi-step marketing workflow without managing API credentials directly"],"best_for":["Marketing teams building LLM-powered audience analysis workflows","Solo developers prototyping audience segmentation agents","Teams migrating from direct Audiense API calls to MCP-based tool orchestration"],"limitations":["Requires valid Audiense API credentials and active subscription to access underlying data","MCP transport adds request/response serialization overhead (~50-100ms per query)","No built-in caching of demographic data — each query hits the Audiense API","Limited to demographic dimensions exposed by Audiense; custom segmentation logic must be implemented client-side"],"requires":["Audiense API key and account with Insights product enabled","MCP-compatible client (Claude Desktop, Cline, or custom MCP host)","Network access to Audiense API endpoints"],"input_types":["text (natural language queries like 'show me age distribution for tech audience')","structured parameters (audience ID, segment filters)"],"output_types":["structured JSON (demographic distributions, percentages by segment)","text summaries (natural language descriptions of audience composition)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-audiense-insights__cap_1","uri":"capability://data.processing.analysis.cultural.psychographic.audience.profiling","name":"cultural-psychographic-audience-profiling","description":"Retrieves cultural and psychographic attributes of audiences (values, interests, lifestyle segments, cultural affinities) from Audiense Insights and exposes them as queryable MCP resources. Translates LLM requests into Audiense psychographic API calls, returning structured profiles that describe audience mindsets, cultural preferences, and behavioral patterns beyond demographics.","intents":["Understand the cultural values and lifestyle preferences of a target audience for messaging strategy","Build audience personas that include psychographic data (not just age/location) for content creators","Automatically generate culturally-aligned marketing copy by analyzing audience psychographic profiles"],"best_for":["Content creators and copywriters building LLM-assisted persona development","Marketing strategists using AI to align messaging with audience values","Agencies automating audience insight generation for client proposals"],"limitations":["Psychographic data quality depends on Audiense's underlying data sources and modeling; may not capture niche subcultures","No real-time updates — psychographic profiles are periodic snapshots, not live streams","Cultural categorizations are Audiense-proprietary and may not align with custom taxonomy or regional nuances","Requires interpretation; raw psychographic data is categorical and may need LLM post-processing for narrative coherence"],"requires":["Audiense API key with Insights product subscription","MCP client with tool execution capability","Audience ID or social handle to query against"],"input_types":["text (audience identifier, e.g., 'analyze psychographics of @TechCrunch followers')","structured parameters (segment filters, cultural category preferences)"],"output_types":["structured JSON (psychographic attributes, cultural segments, lifestyle categories with confidence scores)","narrative text (LLM-generated persona descriptions based on psychographic data)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-audiense-insights__cap_2","uri":"capability://search.retrieval.influencer.identification.and.ranking","name":"influencer-identification-and-ranking","description":"Queries Audiense's influencer database to identify and rank influential accounts within a target audience, returning influencer profiles with reach, engagement metrics, and audience overlap. Implements MCP tools that translate LLM requests into Audiense influencer API calls, filtering by niche, follower count, engagement rate, and audience alignment to surface relevant micro and macro influencers.","intents":["Find micro-influencers in a specific niche who align with a brand's target audience","Automatically rank influencers by engagement quality and audience overlap for partnership outreach","Build an influencer discovery agent that filters candidates by multiple criteria (reach, engagement, audience demographics)"],"best_for":["Influencer marketing teams automating partnership discovery","Agencies building influencer recommendation engines for clients","Brands using LLM agents to shortlist influencers for campaign outreach"],"limitations":["Influencer rankings are based on Audiense's proprietary scoring; may not reflect emerging or niche influencers outside their dataset","Engagement metrics are periodic snapshots, not real-time; influencer performance can shift between updates","No direct contact information provided; requires separate outreach workflow or CRM integration","Audience overlap calculations assume Audiense has sufficient data on both the influencer and target audience"],"requires":["Audiense API key with Insights product and influencer module enabled","MCP client with tool execution","Target audience definition (e.g., audience ID or social handle)"],"input_types":["text (natural language queries like 'find fitness influencers with 50k-500k followers')","structured parameters (niche, follower range, engagement thresholds, audience overlap minimum)"],"output_types":["structured JSON (influencer profiles with handle, follower count, engagement rate, audience overlap %)","ranked lists (influencers sorted by relevance, reach, or engagement)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-audiense-insights__cap_3","uri":"capability://data.processing.analysis.content.engagement.pattern.analysis","name":"content-engagement-pattern-analysis","description":"Analyzes content performance and engagement patterns within a target audience, returning insights on which content types, topics, and formats drive engagement. Implements MCP tools that query Audiense's content engagement data, identifying trending topics, optimal posting times, and content preferences specific to an audience segment.","intents":["Identify which content topics and formats resonate most with a target audience","Determine optimal posting times and content cadence for audience engagement","Automatically generate content recommendations based on audience engagement patterns"],"best_for":["Content creators and social media managers optimizing posting strategy","Marketing teams building data-driven content calendars","LLM-powered content recommendation systems"],"limitations":["Engagement data reflects historical patterns; may not predict emerging trends or viral moments","Content categorization is Audiense-proprietary; custom content taxonomies require post-processing","Optimal posting times are aggregate recommendations; individual account performance may vary","No causal analysis — tool identifies correlations between content and engagement, not root causes"],"requires":["Audiense API key with Insights product enabled","MCP client with tool execution","Audience ID or social handle to analyze"],"input_types":["text (audience identifier, content type filters like 'video', 'news', 'entertainment')","structured parameters (time period, engagement metric type, content category)"],"output_types":["structured JSON (content topics with engagement metrics, posting time recommendations, content format performance)","narrative insights (LLM-generated content strategy recommendations)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-audiense-insights__cap_4","uri":"capability://planning.reasoning.multi.tool.audience.intelligence.orchestration","name":"multi-tool-audience-intelligence-orchestration","description":"Orchestrates multiple Audiense MCP tools (demographics, psychographics, influencers, content engagement) within a single LLM agent workflow, enabling complex audience analysis that combines insights from multiple data sources. Implements MCP's tool composition pattern, allowing Claude and other agents to chain demographic queries with psychographic analysis and influencer discovery in a single multi-step reasoning process.","intents":["Build a comprehensive audience report that combines demographics, psychographics, influencer recommendations, and content strategy","Create an LLM agent that automatically generates marketing briefs by synthesizing multiple Audiense insights","Develop a multi-step workflow that uses demographic data to filter influencers, then analyzes their audience psychographics"],"best_for":["Marketing agencies building end-to-end audience intelligence workflows","Teams automating audience research and strategy generation","Developers building LLM agents that require multi-source audience data"],"limitations":["Tool composition adds latency; each step in the workflow requires a separate API call to Audiense (~100-200ms per step)","No built-in data deduplication or conflict resolution if insights from different tools contradict","Requires careful prompt engineering to ensure LLM chains tools in logical order and interprets results correctly","No transaction semantics — if one tool fails mid-workflow, the agent must handle partial results"],"requires":["Audiense API key with multiple product modules enabled (Insights, Influencers, Content)","MCP client with multi-tool support and context window large enough for chained queries","LLM with strong reasoning capability (Claude 3+, GPT-4, or equivalent)"],"input_types":["text (high-level audience analysis request like 'build a marketing strategy for tech startup audience')","structured parameters (audience ID, analysis scope, output format preferences)"],"output_types":["structured JSON (comprehensive audience profile with demographics, psychographics, influencer recommendations, content strategy)","narrative reports (marketing briefs, audience personas, strategy recommendations)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Audiense API key and account with Insights product enabled","MCP-compatible client (Claude Desktop, Cline, or custom MCP host)","Network access to Audiense API endpoints","Audiense API key with Insights product subscription","MCP client with tool execution capability","Audience ID or social handle to query against","Audiense API key with Insights product and influencer module enabled","MCP client with tool execution","Target audience definition (e.g., audience ID or social handle)","Audiense API key with Insights product enabled"],"failure_modes":["Requires valid Audiense API credentials and active subscription to access underlying data","MCP transport adds request/response serialization overhead (~50-100ms per query)","No built-in caching of demographic data — each query hits the Audiense API","Limited to demographic dimensions exposed by Audiense; 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