{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm_npm-modelcontextprotocolserver-cohort-heatmap","slug":"npm-modelcontextprotocolserver-cohort-heatmap","name":"@modelcontextprotocol/server-cohort-heatmap","type":"mcp","url":"https://www.npmjs.com/package/@modelcontextprotocol/server-cohort-heatmap","page_url":"https://unfragile.ai/npm-modelcontextprotocolserver-cohort-heatmap","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm_npm-modelcontextprotocolserver-cohort-heatmap__cap_0","uri":"capability://data.processing.analysis.cohort.based.retention.heatmap.generation","name":"cohort-based retention heatmap generation","description":"Generates interactive retention heatmaps by organizing users into cohorts (grouped by signup/activation date) and tracking their engagement metrics across time periods. The server implements a cohort analysis engine that accepts raw event data, buckets users into temporal cohorts, calculates retention rates per cohort-period intersection, and renders the data as a structured heatmap matrix suitable for visualization. This enables product teams to identify retention patterns and cohort-specific engagement trends without manual data aggregation.","intents":["I need to visualize how different user cohorts (by signup month) retain over time to identify which cohorts are healthier","I want to generate retention heatmaps programmatically from my event data to embed in dashboards or reports","I need to compare retention curves across cohorts to understand if product changes improved retention for newer users"],"best_for":["product analytics teams building retention dashboards","founders and growth teams analyzing user lifecycle metrics","data engineers integrating cohort analysis into BI pipelines"],"limitations":["Requires pre-aggregated or raw event data with user IDs and timestamps — no built-in data ingestion from analytics platforms","Heatmap granularity (daily/weekly/monthly cohorts) must be specified at query time; no dynamic bucketing","No built-in support for multi-dimensional cohorts (e.g., by geography AND signup source simultaneously)","Output is structured data only; visualization rendering delegated to client-side tools"],"requires":["Node.js 16+ (MCP server runtime)","Event data with at minimum: user_id, event_timestamp, cohort_identifier (or derivable from signup_date)","MCP client capable of calling server tools (Claude Desktop, custom MCP client, or LLM with MCP support)"],"input_types":["structured event data (JSON arrays or objects)","user cohort definitions (date ranges or explicit cohort IDs)","retention metric definitions (e.g., 'active in period' = any event)"],"output_types":["structured heatmap matrix (cohort × time_period grid with retention percentages)","JSON-serialized retention data","aggregated cohort statistics (size, retention rate, churn rate)"],"categories":["data-processing-analysis","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-modelcontextprotocolserver-cohort-heatmap__cap_1","uri":"capability://data.processing.analysis.temporal.cohort.bucketing.and.aggregation","name":"temporal cohort bucketing and aggregation","description":"Partitions users into cohorts based on temporal boundaries (e.g., signup week, activation month) and aggregates engagement metrics within each cohort-period cell. The implementation accepts raw event streams, applies configurable time-window functions to assign users to cohorts, and computes retention/engagement statistics per cohort without requiring pre-computed aggregations. This enables flexible cohort definitions and supports ad-hoc analysis without data warehouse dependencies.","intents":["I want to bucket users by their signup week and see how many were active in each subsequent week","I need to compare monthly cohorts with different retention calculation rules (e.g., 'any event' vs 'paid action')","I want to dynamically adjust cohort granularity (daily vs weekly vs monthly) without re-running data pipelines"],"best_for":["analytics engineers prototyping cohort definitions before building warehouse models","product managers exploring retention patterns with different cohort boundaries","AI agents autonomously analyzing user lifecycle data"],"limitations":["Bucketing logic is applied in-memory; large datasets (>1M events) may require pagination or streaming","No support for overlapping cohorts or rolling-window cohorts (e.g., 'users active in last 7 days')","Cohort boundaries are fixed at query time; cannot retroactively redefine cohorts across historical data"],"requires":["Event data with user_id and timestamp fields","Cohort definition parameters: start_date, bucket_size (day/week/month), retention_metric_definition"],"input_types":["event array with user_id and timestamp","cohort bucketing configuration (time unit, start date)","retention metric definition (filter or aggregation function)"],"output_types":["cohort membership mapping (user_id → cohort_id)","per-cohort aggregated metrics (size, retention rate by period)"],"categories":["data-processing-analysis","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-modelcontextprotocolserver-cohort-heatmap__cap_2","uri":"capability://data.processing.analysis.retention.metric.calculation.and.comparison","name":"retention metric calculation and comparison","description":"Computes retention rates, churn rates, and engagement metrics across cohort-period intersections using configurable metric definitions. The server accepts event data and metric specifications (e.g., 'user is retained if they had any event in the period'), calculates the metric for each cohort-period cell, and returns a normalized heatmap suitable for visualization. Supports multiple retention definitions (e.g., DAU-based, transaction-based, feature-specific) without requiring separate data pipelines.","intents":["I need to calculate week-over-week retention for each cohort to identify which cohorts are degrading","I want to compare retention using different definitions (any event vs paid action) to understand monetization impact","I need to generate retention curves for specific cohorts to present to stakeholders"],"best_for":["product teams analyzing retention trends and cohort health","data analysts building retention reports without SQL","AI agents autonomously generating retention insights"],"limitations":["Metric definitions must be specified upfront; no support for ad-hoc metric discovery","Retention calculations assume binary engagement (active/inactive per period); no support for intensity metrics (e.g., engagement score)","No built-in statistical significance testing or confidence intervals"],"requires":["Event data with user_id, timestamp, and optional event_type or action fields","Metric definition: aggregation function or filter (e.g., 'count(events) > 0')"],"input_types":["event array with user_id, timestamp, and optional metadata","metric definition (filter expression or aggregation function)","cohort and period definitions"],"output_types":["retention rate matrix (cohort × period with percentages)","churn rate matrix","per-cohort retention curves"],"categories":["data-processing-analysis","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-modelcontextprotocolserver-cohort-heatmap__cap_3","uri":"capability://tool.use.integration.mcp.tool.exposure.for.cohort.analysis.operations","name":"mcp tool exposure for cohort analysis operations","description":"Exposes cohort analysis capabilities as MCP server tools, enabling LLM clients and AI agents to invoke cohort generation, retention calculation, and heatmap rendering through the Model Context Protocol. The server implements tool schemas that define input parameters (event data, cohort config, metric definitions) and output formats, allowing Claude and other MCP-compatible clients to autonomously call these tools within agentic workflows. This enables conversational data analysis where users describe retention questions in natural language and the agent executes the appropriate analysis.","intents":["I want to ask Claude 'what's the retention trend for users who signed up in Q1?' and have it automatically generate the heatmap","I need to build an AI agent that can autonomously analyze retention and suggest product improvements","I want to integrate cohort analysis into a larger MCP-based data analysis workflow"],"best_for":["teams using Claude or other MCP-compatible LLMs for data analysis","builders creating AI agents that need autonomous analytics capabilities","organizations standardizing on MCP for tool integration"],"limitations":["Requires MCP client support; not directly usable via REST APIs or traditional integrations","Tool invocation latency depends on MCP client and server communication overhead","No built-in authentication or access control; relies on MCP client-level security"],"requires":["MCP-compatible client (Claude Desktop, custom MCP client, or LLM with MCP support)","MCP server running (Node.js 16+)","Event data accessible to the server (local file, database connection, or API)"],"input_types":["MCP tool call with parameters: event_data, cohort_config, metric_definitions","natural language prompts (interpreted by LLM client)"],"output_types":["structured heatmap data (JSON)","tool execution results with metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-modelcontextprotocolserver-cohort-heatmap__cap_4","uri":"capability://data.processing.analysis.heatmap.data.structure.generation.and.serialization","name":"heatmap data structure generation and serialization","description":"Transforms aggregated retention metrics into a structured heatmap matrix (cohort × time_period grid) and serializes it to JSON for downstream visualization or reporting. The implementation organizes retention data into a normalized tabular format with cohort identifiers as rows, time periods as columns, and retention percentages as cell values, optionally including metadata (cohort size, absolute retention counts). This enables consistent data exchange between the analysis engine and visualization tools.","intents":["I need to export retention data in a format that my visualization library (D3, Plotly, etc.) can consume","I want to embed retention heatmaps in reports or dashboards without custom formatting","I need to compare heatmaps across different analyses by normalizing their structure"],"best_for":["teams building custom retention dashboards","data analysts exporting analysis results for reporting","developers integrating cohort analysis into larger data pipelines"],"limitations":["Output format is fixed to a matrix structure; no support for alternative layouts (e.g., long-form tables)","Large heatmaps (>1000 cohorts × 100 periods) may produce large JSON payloads","No built-in compression or streaming; entire heatmap must be serialized at once"],"requires":["Aggregated retention metrics (cohort × period matrix)","Optional: metadata (cohort sizes, absolute counts)"],"input_types":["aggregated retention data (numeric matrix)","cohort and period identifiers"],"output_types":["JSON heatmap structure with cohort rows, period columns, and retention values","optional metadata (cohort sizes, total users)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ (MCP server runtime)","Event data with at minimum: user_id, event_timestamp, cohort_identifier (or derivable from signup_date)","MCP client capable of calling server tools (Claude Desktop, custom MCP client, or LLM with MCP support)","Event data with user_id and timestamp fields","Cohort definition parameters: start_date, bucket_size (day/week/month), retention_metric_definition","Event data with user_id, timestamp, and optional event_type or action fields","Metric definition: aggregation function or filter (e.g., 'count(events) > 0')","MCP-compatible client (Claude Desktop, custom MCP client, or LLM with MCP support)","MCP server running (Node.js 16+)","Event data accessible to the server (local file, database connection, or API)"],"failure_modes":["Requires pre-aggregated or raw event data with user IDs and timestamps — no built-in data ingestion from analytics platforms","Heatmap granularity (daily/weekly/monthly cohorts) must be specified at query time; no dynamic bucketing","No built-in support for multi-dimensional cohorts (e.g., by geography AND signup source simultaneously)","Output is structured data only; visualization rendering delegated to client-side tools","Bucketing logic is applied in-memory; large datasets (>1M events) may require pagination or streaming","No support for overlapping cohorts or rolling-window cohorts (e.g., 'users active in last 7 days')","Cohort boundaries are fixed at query time; cannot retroactively redefine cohorts across historical data","Metric definitions must be specified upfront; no support for ad-hoc metric discovery","Retention calculations assume binary engagement (active/inactive per period); no support for intensity metrics (e.g., engagement score)","No built-in statistical significance testing or confidence intervals","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"ecosystem":0.3,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:23.904Z","last_scraped_at":"2026-05-03T14:23:45.437Z","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=npm-modelcontextprotocolserver-cohort-heatmap","compare_url":"https://unfragile.ai/compare?artifact=npm-modelcontextprotocolserver-cohort-heatmap"}},"signature":"GRLCtqYFr1Z/ZwneNav3lW8eyoxtz9y9tqepL/bcNXS2DWc7Vvqui1jTIU+MiCjWWVoAqr/OOqIb56oA5WW1CA==","signedAt":"2026-06-19T18:34:35.436Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-modelcontextprotocolserver-cohort-heatmap","artifact":"https://unfragile.ai/npm-modelcontextprotocolserver-cohort-heatmap","verify":"https://unfragile.ai/api/v1/verify?slug=npm-modelcontextprotocolserver-cohort-heatmap","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"}}