{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm_npm-modelcontextprotocolserver-customer-segmentation","slug":"npm-modelcontextprotocolserver-customer-segmentation","name":"@modelcontextprotocol/server-customer-segmentation","type":"mcp","url":"https://www.npmjs.com/package/@modelcontextprotocol/server-customer-segmentation","page_url":"https://unfragile.ai/npm-modelcontextprotocolserver-customer-segmentation","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm_npm-modelcontextprotocolserver-customer-segmentation__cap_0","uri":"capability://tool.use.integration.mcp.server.initialization.with.customer.data.source.binding","name":"mcp server initialization with customer data source binding","description":"Initializes a Model Context Protocol server that exposes customer segmentation operations through the MCP transport layer, binding to a customer data source (JSON, CSV, or database) and registering tools for LLM clients to invoke. Uses MCP's resource and tool registration patterns to advertise segmentation capabilities to connected Claude instances or other MCP-compatible clients.","intents":["Set up an MCP server that exposes customer segmentation as LLM-callable tools","Connect a customer dataset to an MCP transport for remote LLM access","Register segmentation operations as discoverable MCP tools for AI agents"],"best_for":["AI agent builders integrating customer data into multi-step workflows","Teams deploying LLM-driven customer analytics as a service","Developers building Claude-powered CRM or marketing automation systems"],"limitations":["No built-in authentication — relies on MCP client-side security and transport-layer encryption","Single-threaded request handling by default — requires external load balancing for concurrent segmentation requests","Data source must fit in memory or be streamed; no lazy-loading for very large datasets (>1GB)"],"requires":["Node.js 16+","@modelcontextprotocol/sdk package installed","Customer dataset in JSON, CSV, or queryable database format","MCP client (Claude desktop, custom agent, or compatible tool)"],"input_types":["JSON customer records","CSV files with customer attributes","Database connection strings (if adapter provided)"],"output_types":["MCP tool definitions (JSON schema)","Segmentation results (JSON)","Resource metadata (MCP resource format)"],"categories":["tool-use-integration","mcp-server"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-modelcontextprotocolserver-customer-segmentation__cap_1","uri":"capability://data.processing.analysis.rule.based.customer.segmentation.with.filtering","name":"rule-based customer segmentation with filtering","description":"Executes segmentation logic by applying user-defined or pre-built rules (e.g., RFM scoring, demographic filters, behavioral thresholds) to customer records, returning filtered cohorts. Rules are evaluated using a predicate-matching engine that supports AND/OR logic, numeric comparisons, string matching, and date ranges, enabling LLM clients to dynamically construct segmentation queries without code changes.","intents":["Segment customers by RFM (recency, frequency, monetary) scores or custom metrics","Filter customers by demographic, behavioral, or transactional attributes","Create dynamic audience cohorts based on LLM-generated rule specifications","Apply multi-condition filters (e.g., high-value + recent activity + specific region)"],"best_for":["Marketing teams using AI agents to identify campaign audiences","Product managers building LLM-driven customer analytics dashboards","Developers creating no-code segmentation tools powered by LLMs"],"limitations":["Rule evaluation is in-memory and synchronous — performance degrades with >100k customer records on standard hardware","No support for complex statistical models (clustering, propensity scoring) — limited to rule-based logic","Rules must be pre-defined or generated as JSON; no natural language rule parsing built-in","No caching of segmentation results — each query re-evaluates the full dataset"],"requires":["Customer dataset loaded into server memory","Rule definitions in JSON format (e.g., {field: 'revenue', operator: '>', value: 1000})","Numeric, string, or date fields in customer records for filtering"],"input_types":["JSON rule objects with field, operator, and value","Customer record arrays with typed attributes","Composite rules with AND/OR boolean logic"],"output_types":["Filtered customer arrays (JSON)","Segment metadata (count, average metrics)","Rule execution logs (for debugging)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-modelcontextprotocolserver-customer-segmentation__cap_2","uri":"capability://memory.knowledge.dynamic.segment.definition.and.persistence","name":"dynamic segment definition and persistence","description":"Allows creation, storage, and retrieval of named customer segments with rule definitions, enabling LLM clients to save segmentation logic for reuse across multiple requests. Segments are persisted in a local JSON file or optional external store, with metadata tracking creation date, rule version, and segment size. Supports CRUD operations (create, read, update, delete) on segment definitions via MCP tools.","intents":["Save a customer segment definition for repeated use in campaigns or analysis","Retrieve previously defined segments without re-specifying rules","Update segment rules when business logic changes","List all available segments and their metadata for discovery"],"best_for":["Marketing automation platforms that need persistent audience definitions","Teams building LLM-driven CRM systems with reusable segment libraries","Analysts who want to version-control segment definitions alongside code"],"limitations":["Persistence is file-based by default — no distributed state management or multi-instance synchronization","No audit logging or change history — updates overwrite previous definitions","Segment size is computed at retrieval time, not cached — large datasets cause latency","No role-based access control — all MCP clients can read/write all segments"],"requires":["Write access to filesystem (for JSON persistence) or external database connection","Segment name and rule definition in JSON format","MCP client capable of invoking tool calls"],"input_types":["Segment name (string)","Rule definition (JSON object)","Metadata (optional: description, tags, owner)"],"output_types":["Segment metadata (name, rule, size, created_at)","List of all segments (JSON array)","Success/error response (JSON)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-modelcontextprotocolserver-customer-segmentation__cap_3","uri":"capability://data.processing.analysis.segment.composition.and.boolean.logic","name":"segment composition and boolean logic","description":"Enables combining multiple saved segments using boolean operators (union, intersection, difference) to create composite audiences. Implements set-based operations on segment membership, allowing LLM clients to express complex audience logic (e.g., 'high-value AND recent AND not-churned') by composing pre-defined segments. Operations are evaluated lazily to minimize redundant filtering.","intents":["Combine multiple segments to create complex audience definitions","Find customers in segment A but not in segment B (exclusion logic)","Create overlapping audiences for A/B testing or multi-channel campaigns","Express audience logic in natural language that maps to segment composition"],"best_for":["Campaign managers building sophisticated audience targeting rules","Product teams running multi-variant experiments with overlapping cohorts","LLM agents that need to reason about audience composition in natural language"],"limitations":["Composition operations require all referenced segments to exist — no automatic fallback or partial evaluation","No optimization for complex nested compositions — deeply nested boolean logic may cause performance degradation","Set operations assume segment membership is deterministic — no probabilistic or fuzzy matching","Results are computed on-demand; no pre-computed composition caching"],"requires":["Two or more saved segments to compose","Boolean operator specification (AND, OR, NOT, MINUS)","Customer dataset loaded in memory"],"input_types":["Segment names (strings)","Boolean operator (AND, OR, NOT, MINUS)","Nested composition expressions (JSON tree)"],"output_types":["Composite segment membership (customer array)","Composition metadata (size, overlap statistics)","Execution plan (for debugging)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-modelcontextprotocolserver-customer-segmentation__cap_4","uri":"capability://data.processing.analysis.segment.analytics.and.metrics.computation","name":"segment analytics and metrics computation","description":"Computes aggregate statistics and metrics for a segment or set of segments, including count, average/median/percentile values, distribution histograms, and trend analysis. Metrics are calculated in-memory using streaming aggregation to minimize memory overhead, and results are returned as structured JSON suitable for visualization or reporting. Supports grouping by attributes (e.g., metrics by region or cohort).","intents":["Get summary statistics for a customer segment (size, average lifetime value, churn rate)","Compare metrics across multiple segments to identify differences","Generate segment profiles for reporting or audience documentation","Compute cohort-level metrics for A/B test analysis"],"best_for":["Analysts building segment reports and dashboards","Marketing teams evaluating segment quality before campaign launch","Data scientists using LLM agents to explore customer data interactively"],"limitations":["Metrics are computed at query time — no pre-aggregation or materialized views","Percentile calculations use approximate algorithms (T-Digest or similar) for large datasets, introducing ~1-5% error","No support for time-series metrics or trend forecasting — only point-in-time snapshots","Grouping by high-cardinality attributes (e.g., customer ID) may cause memory exhaustion"],"requires":["Segment definition or customer array","Numeric or categorical fields for aggregation","Metric specification (count, mean, median, percentile, histogram)"],"input_types":["Segment name or customer array","Metric names (count, mean, median, p50, p95, histogram)","Grouping attribute (optional, for stratified metrics)"],"output_types":["Metric object (JSON with numeric values)","Distribution histogram (JSON array of bins)","Grouped metrics (JSON object keyed by group value)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-modelcontextprotocolserver-customer-segmentation__cap_5","uri":"capability://data.processing.analysis.segment.export.and.format.conversion","name":"segment export and format conversion","description":"Exports segment membership (customer lists) in multiple formats (JSON, CSV, Parquet, or custom delimited formats) suitable for downstream systems like email platforms, data warehouses, or analytics tools. Supports field selection, sorting, and pagination to handle large segments without memory exhaustion. Exports can be streamed to files or returned as base64-encoded data for embedding in MCP responses.","intents":["Export a customer segment to CSV for upload to an email marketing platform","Generate a Parquet file of segment data for loading into a data warehouse","Retrieve a subset of segment members with specific fields for API integration","Create downloadable segment reports in multiple formats"],"best_for":["Marketing operations teams integrating segments with external platforms","Data engineers building ETL pipelines that consume segment exports","Developers creating segment download features in customer-facing applications"],"limitations":["Large exports (>100k rows) may exceed MCP message size limits — requires streaming or pagination","Format conversion adds ~5-10% overhead compared to native format","No built-in compression — large exports consume significant bandwidth","Field selection is static per export — no dynamic field mapping based on destination system"],"requires":["Segment definition or customer array","Target export format (JSON, CSV, Parquet, TSV, etc.)","Optional: field list, sort order, pagination parameters"],"input_types":["Segment name or customer array","Export format (json, csv, parquet, tsv)","Field selection (array of field names)","Pagination (limit, offset)"],"output_types":["Exported data (file path or base64-encoded content)","Export metadata (row count, file size, format)","Streaming response (for large exports)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-modelcontextprotocolserver-customer-segmentation__cap_6","uri":"capability://safety.moderation.segment.validation.and.quality.checks","name":"segment validation and quality checks","description":"Validates segment definitions for correctness and quality, checking for issues like empty segments, invalid rule syntax, circular dependencies, or data quality problems (missing values, outliers). Runs automated checks and returns a quality report with warnings and recommendations. Supports custom validation rules defined by the user.","intents":["Verify a segment definition is syntactically correct before saving","Detect data quality issues that might affect segmentation accuracy","Identify segments that are too small or too large for effective use","Validate that segment rules align with business logic"],"best_for":["Data quality teams ensuring segment definitions meet standards","LLM agents that need to validate generated segment rules before execution","Teams building segment governance and compliance workflows"],"limitations":["Validation is rule-based and heuristic — cannot detect all logical errors","Custom validation rules require code changes; no dynamic rule definition","No statistical validation (e.g., detecting Simpson's paradox or confounding variables)","Validation results are advisory only — no enforcement mechanism"],"requires":["Segment definition (rule object)","Customer dataset for validation checks","Optional: custom validation rule definitions"],"input_types":["Segment name or rule definition","Validation rule set (optional)","Customer dataset (for data quality checks)"],"output_types":["Validation report (JSON with errors, warnings, recommendations)","Quality score (0-100)","Suggested fixes (if applicable)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+","@modelcontextprotocol/sdk package installed","Customer dataset in JSON, CSV, or queryable database format","MCP client (Claude desktop, custom agent, or compatible tool)","Customer dataset loaded into server memory","Rule definitions in JSON format (e.g., {field: 'revenue', operator: '>', value: 1000})","Numeric, string, or date fields in customer records for filtering","Write access to filesystem (for JSON persistence) or external database connection","Segment name and rule definition in JSON format","MCP client capable of invoking tool calls"],"failure_modes":["No built-in authentication — relies on MCP client-side security and transport-layer encryption","Single-threaded request handling by default — requires external load balancing for concurrent segmentation requests","Data source must fit in memory or be streamed; no lazy-loading for very large datasets (>1GB)","Rule evaluation is in-memory and synchronous — performance degrades with >100k customer records on standard hardware","No support for complex statistical models (clustering, propensity scoring) — limited to rule-based logic","Rules must be pre-defined or generated as JSON; no natural language rule parsing built-in","No caching of segmentation results — each query re-evaluates the full dataset","Persistence is file-based by default — no distributed state management or multi-instance synchronization","No audit logging or change history — updates overwrite previous definitions","Segment size is computed at retrieval time, not cached — large datasets cause latency","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.24,"ecosystem":0.3,"match_graph":0.25,"freshness":0.9,"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":"2026-05-03T14:23:46.039Z","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-customer-segmentation","compare_url":"https://unfragile.ai/compare?artifact=npm-modelcontextprotocolserver-customer-segmentation"}},"signature":"5tf+LVnJimXHxCKOP+qO7JGpFqlEyC1VWAs3Hcdge4yADy3gXkmn6nQURcuWv8BwOwbi6SjFfAgBEP5C97csDA==","signedAt":"2026-06-15T21:22:49.877Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-modelcontextprotocolserver-customer-segmentation","artifact":"https://unfragile.ai/npm-modelcontextprotocolserver-customer-segmentation","verify":"https://unfragile.ai/api/v1/verify?slug=npm-modelcontextprotocolserver-customer-segmentation","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"}}