{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_agkoisine-social-listening","slug":"agkoisine-social-listening","name":"social-listening","type":"mcp","url":"https://smithery.ai/servers/agkoisine/social-listening","page_url":"https://unfragile.ai/agkoisine-social-listening","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:agkoisine/social-listening"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_agkoisine-social-listening__cap_0","uri":"capability://tool.use.integration.multi.platform.social.media.data.ingestion.via.mcp.protocol","name":"multi-platform social media data ingestion via mcp protocol","description":"Implements a Model Context Protocol server that standardizes connections to multiple social media platforms (Twitter/X, Instagram, TikTok, LinkedIn, etc.) through a unified MCP interface. Uses MCP's resource and tool abstractions to expose platform-specific APIs as composable, context-aware tools that Claude and other MCP-compatible clients can invoke. The server handles authentication token management, rate-limit coordination, and platform-specific payload normalization into a common schema.","intents":["I want to monitor mentions of my brand across Twitter, Instagram, and LinkedIn simultaneously without writing platform-specific integration code","I need to build an AI agent that can search social media and analyze sentiment in real-time as part of a larger workflow","I want to give Claude direct access to social listening capabilities without managing separate API clients"],"best_for":["AI agent builders integrating social listening into multi-step workflows","teams building brand monitoring dashboards with Claude as the analysis engine","developers prototyping social media analysis features without platform-specific SDK overhead"],"limitations":["Rate limits are platform-specific and may cause request queuing or failures during high-volume monitoring","Authentication requires valid API credentials for each platform, adding operational complexity","Real-time data freshness depends on platform API latency; historical data may be limited by platform retention policies","No built-in caching layer — repeated queries to the same data will incur platform API costs"],"requires":["MCP-compatible client (Claude Desktop, or custom MCP host)","Valid API credentials for target social platforms (Twitter API v2 key, Instagram Graph API token, etc.)","Network connectivity to platform APIs","Node.js 16+ or Python 3.8+ (depending on server implementation)"],"input_types":["search queries (text)","hashtags (text)","account handles (text)","date ranges (structured)","filter parameters (structured)"],"output_types":["posts/tweets (structured JSON with metadata)","user profiles (structured JSON)","engagement metrics (numeric)","media attachments (URLs, image metadata)"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_agkoisine-social-listening__cap_1","uri":"capability://search.retrieval.real.time.social.media.search.with.keyword.and.entity.filtering","name":"real-time social media search with keyword and entity filtering","description":"Exposes search capabilities across social platforms with support for keyword queries, hashtag matching, account/user filtering, and temporal constraints. Implements query translation to platform-specific search syntax (Twitter's PowerTrack, Instagram's hashtag API, etc.) and aggregates results with consistent ranking/sorting. Handles pagination and result deduplication when querying multiple platforms simultaneously.","intents":["I want to search for all mentions of a specific product name across social media in the last 24 hours","I need to find posts from a specific set of influencers discussing a particular topic","I want to monitor a hashtag campaign and retrieve the top posts by engagement"],"best_for":["brand monitoring teams tracking product mentions and competitor activity","marketing analysts researching campaign performance and audience sentiment","crisis management teams responding to negative mentions in real-time"],"limitations":["Search depth varies by platform — Twitter API v2 free tier limited to 7 days of historical data, while Instagram Graph API has stricter rate limits","Keyword matching is platform-dependent; some platforms support regex or boolean operators while others only support simple substring matching","Results may include retweets, reposts, and duplicates that require client-side deduplication logic","Pagination tokens expire after a platform-specific duration, requiring re-query if resuming after delay"],"requires":["MCP client with tool-calling support","Platform-specific API credentials with search permissions","Query string formatted according to platform search syntax (or MCP server handles translation)"],"input_types":["search query (text, may include keywords, hashtags, operators)","filters (structured: date range, language, location, account type)","pagination parameters (offset, limit)"],"output_types":["posts/tweets (structured JSON with text, author, timestamp, engagement metrics)","pagination tokens (opaque string for resuming results)","aggregated metadata (total result count, search execution time)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_agkoisine-social-listening__cap_2","uri":"capability://data.processing.analysis.social.media.sentiment.and.engagement.analysis.with.metadata.extraction","name":"social media sentiment and engagement analysis with metadata extraction","description":"Analyzes posts retrieved from social platforms to extract sentiment (positive/negative/neutral), engagement metrics (likes, shares, comments, reach), and structured metadata (author influence, post type, media presence). Implements NLP-based sentiment classification (may use rule-based scoring, ML models, or platform-native sentiment APIs) and aggregates engagement data with time-series tracking. Extracts hashtags, mentions, URLs, and media references for downstream analysis.","intents":["I want to analyze the sentiment of posts mentioning my brand to understand customer perception","I need to identify the most engaging posts in a campaign and understand what drives engagement","I want to track how sentiment toward a topic evolves over time across social platforms"],"best_for":["brand managers analyzing customer sentiment and brand health metrics","content strategists identifying high-performing post types and engagement drivers","researchers studying public opinion trends on specific topics"],"limitations":["Sentiment analysis accuracy varies by language, dialect, and sarcasm detection — may misclassify ironic or context-dependent posts","Engagement metrics are snapshot-based and may not reflect final engagement if analyzed shortly after posting","Influence scoring (author follower count, historical engagement) requires additional API calls and may be outdated","Media analysis (images, videos) requires separate vision/video processing capabilities not included in text-only sentiment analysis"],"requires":["Posts in structured format with text content (from social-listening search capability)","NLP/sentiment model (built-in, external API, or local model)","Optional: platform-native engagement metrics API access for real-time data"],"input_types":["post text (string)","post metadata (author, timestamp, platform)","optional: post media URLs"],"output_types":["sentiment score (numeric: -1 to 1, or categorical: positive/negative/neutral)","engagement metrics (numeric: likes, shares, comments, reach)","extracted entities (hashtags, mentions, URLs as arrays)","metadata (author influence score, post type classification)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_agkoisine-social-listening__cap_3","uri":"capability://data.processing.analysis.trend.detection.and.topic.clustering.from.social.media.streams","name":"trend detection and topic clustering from social media streams","description":"Identifies emerging topics and trends from aggregated social media data by clustering posts with similar keywords, hashtags, or semantic content. Implements topic modeling (LDA, clustering algorithms) or keyword frequency analysis to surface trending discussions. Tracks trend velocity (growth rate), peak timing, and geographic/demographic distribution. Provides time-series data showing trend emergence and decay.","intents":["I want to detect emerging topics related to my industry before they become mainstream","I need to identify which hashtags or topics are trending in my target audience right now","I want to understand how a specific trend is evolving and when it peaked"],"best_for":["social media strategists planning content calendars based on trending topics","PR teams identifying emerging narratives and potential crises early","researchers studying information diffusion and trend lifecycle"],"limitations":["Trend detection requires sufficient data volume — sparse or niche topics may not cluster reliably","Clustering quality depends on algorithm choice and hyperparameters; may produce false positives or miss subtle trends","Time-series data is only as fresh as the underlying social media data ingestion — real-time trends may lag by minutes to hours","Geographic/demographic trend distribution requires platform-specific metadata that may not be available for all posts"],"requires":["Aggregated post data from multiple time windows (requires social-listening search capability)","Clustering or topic modeling algorithm (built-in or external service)","Sufficient post volume for statistical significance (typically 100+ posts per trend)"],"input_types":["post collection (array of structured posts with text, timestamp, metadata)","time window (date range for trend analysis)","optional: filtering parameters (language, geography, platform)"],"output_types":["trend list (array of topics with keywords, hashtags, representative posts)","trend metrics (velocity, peak time, total volume, engagement)","time-series data (trend strength over time)","geographic/demographic distribution (if available)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_agkoisine-social-listening__cap_4","uri":"capability://data.processing.analysis.influencer.and.account.profiling.with.reach.and.authority.metrics","name":"influencer and account profiling with reach and authority metrics","description":"Retrieves and analyzes social media account profiles to extract influence metrics (follower count, engagement rate, audience demographics, posting frequency, content categories). Implements authority scoring based on follower growth, historical engagement, and network position. Provides audience composition data (age, location, interests) where available from platform APIs. Tracks account activity and content patterns over time.","intents":["I want to identify influencers in my niche who could amplify my campaign message","I need to assess the credibility and reach of accounts discussing my brand","I want to understand the audience demographics of accounts engaging with my content"],"best_for":["influencer marketing teams identifying partnership opportunities and vetting accounts","brand managers assessing account credibility and potential for brand advocacy","competitive intelligence teams analyzing competitor audience and engagement patterns"],"limitations":["Follower count and engagement metrics are snapshots and may be outdated within hours","Audience demographic data is often estimated or incomplete, especially for private accounts","Authority scoring is heuristic-based and may not reflect true influence in niche communities","Account history and content patterns require historical data that may not be available for all platforms"],"requires":["Platform API access with user/account lookup permissions","Account handles or user IDs to profile","Optional: historical data storage for trend analysis"],"input_types":["account handle or user ID (string)","optional: batch of handles (array)","optional: time window for historical analysis"],"output_types":["profile data (name, bio, follower count, following count, verified status)","engagement metrics (average likes/comments per post, engagement rate)","authority score (numeric, platform-specific)","audience demographics (age range, location, interests as structured data)","content analysis (top topics, posting frequency, content types)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_agkoisine-social-listening__cap_5","uri":"capability://data.processing.analysis.competitive.intelligence.and.brand.mention.tracking.with.comparative.analysis","name":"competitive intelligence and brand mention tracking with comparative analysis","description":"Monitors mentions of competitor brands and products alongside your own brand, enabling comparative sentiment and engagement analysis. Implements mention deduplication (e.g., 'brand' vs 'Brand' vs 'BRAND'), competitor identification from a configurable list, and side-by-side metric comparison. Tracks competitive share of voice (percentage of mentions relative to total category mentions) and identifies posts mentioning multiple competitors simultaneously.","intents":["I want to track how my brand is mentioned relative to competitors in the same category","I need to identify posts that mention both my brand and competitors to understand positioning","I want to monitor competitor sentiment and engagement to benchmark my own performance"],"best_for":["competitive intelligence teams monitoring market positioning and messaging","marketing teams benchmarking campaign performance against competitors","product managers tracking feature mentions and competitive differentiation in customer conversations"],"limitations":["Competitor list must be manually configured and maintained — no automatic competitor detection","Share of voice calculation depends on category definition and mention filtering, which may be ambiguous","Mention deduplication is heuristic-based and may miss brand name variations or misspellings","Comparative analysis requires sufficient mention volume for all competitors — sparse data may skew metrics"],"requires":["Configured list of competitor brand names/handles","Social media search capability (from social-listening search)","Time window for comparison (e.g., last 30 days)"],"input_types":["brand list (array of brand names/handles to monitor)","category definition (optional, for share of voice calculation)","time window (date range for comparison)"],"output_types":["mention counts per brand (numeric)","share of voice per brand (percentage)","sentiment distribution per brand (positive/negative/neutral percentages)","engagement metrics per brand (average likes, shares, comments)","comparative posts (posts mentioning multiple brands, with context)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_agkoisine-social-listening__cap_6","uri":"capability://automation.workflow.alert.and.notification.triggering.based.on.social.media.events.and.thresholds","name":"alert and notification triggering based on social media events and thresholds","description":"Monitors social media streams for specific events (mentions of brand/keywords, sentiment spikes, engagement thresholds, influencer activity) and triggers alerts when conditions are met. Implements configurable rules (e.g., 'alert if negative sentiment exceeds 30% in last hour', 'alert if post reaches 1000 likes in 10 minutes'). Supports multiple notification channels (webhook, email, Slack, etc.) and alert deduplication to prevent notification spam.","intents":["I want to be notified immediately when my brand is mentioned by a major influencer","I need to alert my team if sentiment toward my brand drops sharply in a specific time window","I want to trigger an automated response when a post about my product goes viral"],"best_for":["crisis management teams responding to negative mentions or PR issues in real-time","social media managers monitoring campaign performance and engagement spikes","customer service teams identifying customer complaints or support requests on social media"],"limitations":["Alert latency depends on social media data ingestion frequency — real-time alerts may lag by minutes","Rule complexity is limited by MCP tool parameter constraints — complex boolean logic may require multiple rules","Alert deduplication is heuristic-based and may produce false positives or miss related events","Notification delivery is not guaranteed — webhook failures or email spam filters may prevent alerts from reaching recipients"],"requires":["Configured alert rules (threshold values, event types, notification channels)","Notification endpoint (webhook URL, email address, Slack webhook, etc.)","Continuous social media monitoring (requires background polling or event streaming)"],"input_types":["alert rule (structured: event type, condition, threshold, notification channel)","optional: rule list (array of multiple rules)"],"output_types":["alert confirmation (success/failure status)","rule ID (for later modification or deletion)","alert history (past alerts triggered by this rule)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_agkoisine-social-listening__cap_7","uri":"capability://planning.reasoning.content.recommendation.and.posting.optimization.based.on.social.performance.data","name":"content recommendation and posting optimization based on social performance data","description":"Analyzes historical social media performance data (engagement metrics, sentiment, reach) to recommend content types, posting times, hashtags, and messaging strategies. Implements pattern recognition to identify correlations between content attributes (post length, media type, hashtags, posting time) and engagement outcomes. Provides optimization suggestions (e.g., 'posts with 3-5 hashtags get 40% more engagement', 'video content performs 2x better than text-only on this audience').","intents":["I want to know the best time to post content for my audience","I need recommendations on which hashtags and content types drive the most engagement","I want to optimize my messaging based on what resonates with my audience"],"best_for":["content creators and social media managers optimizing posting strategy","marketing teams planning content calendars based on performance data","brands testing messaging variations and measuring impact"],"limitations":["Recommendations are based on historical data and may not generalize to new audiences or market conditions","Correlation analysis may identify spurious patterns (e.g., posts with emoji perform better, but emoji may be correlated with other factors)","Optimal posting time varies by audience segment and platform algorithm changes — recommendations may become stale","Content performance is influenced by external factors (news, trends, platform algorithm changes) not captured in historical data"],"requires":["Historical post data with engagement metrics (requires social-listening search and sentiment analysis)","Sufficient data volume for pattern recognition (typically 100+ posts)","Optional: audience segmentation data for personalized recommendations"],"input_types":["post collection (array of historical posts with text, metadata, engagement metrics)","optional: audience segment (for segment-specific recommendations)","optional: content type filter (e.g., 'video only', 'text only')"],"output_types":["content recommendations (suggested post types, formats, length ranges)","optimal posting times (hour of day, day of week)","hashtag recommendations (list of effective hashtags with performance metrics)","messaging insights (themes, topics, or phrases that drive engagement)","performance correlations (structured data showing attribute → engagement relationships)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["MCP-compatible client (Claude Desktop, or custom MCP host)","Valid API credentials for target social platforms (Twitter API v2 key, Instagram Graph API token, etc.)","Network connectivity to platform APIs","Node.js 16+ or Python 3.8+ (depending on server implementation)","MCP client with tool-calling support","Platform-specific API credentials with search permissions","Query string formatted according to platform search syntax (or MCP server handles translation)","Posts in structured format with text content (from social-listening search capability)","NLP/sentiment model (built-in, external API, or local model)","Optional: platform-native engagement metrics API access for real-time data"],"failure_modes":["Rate limits are platform-specific and may cause request queuing or failures during high-volume monitoring","Authentication requires valid API credentials for each platform, adding operational complexity","Real-time data freshness depends on platform API latency; historical data may be limited by platform retention policies","No built-in caching layer — repeated queries to the same data will incur platform API costs","Search depth varies by platform — Twitter API v2 free tier limited to 7 days of historical data, while Instagram Graph API has stricter rate limits","Keyword matching is platform-dependent; some platforms support regex or boolean operators while others only support simple substring matching","Results may include retweets, reposts, and duplicates that require client-side deduplication logic","Pagination tokens expire after a platform-specific duration, requiring re-query if resuming after delay","Sentiment analysis accuracy varies by language, dialect, and sarcasm detection — may misclassify ironic or context-dependent posts","Engagement metrics are snapshot-based and may not reflect final engagement if analyzed shortly after posting","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.38999999999999996,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.062Z","last_scraped_at":"2026-05-03T15:19:13.222Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=agkoisine-social-listening","compare_url":"https://unfragile.ai/compare?artifact=agkoisine-social-listening"}},"signature":"yq51ht0NK/XiPzF/pwYH0fMQnurQFd3RM4VbKltBL8TOKeJyyA/YLjr78yb77cSuf06wpwh8zL1f4fe7MyQjAg==","signedAt":"2026-06-19T22:24:41.453Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agkoisine-social-listening","artifact":"https://unfragile.ai/agkoisine-social-listening","verify":"https://unfragile.ai/api/v1/verify?slug=agkoisine-social-listening","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}