{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-windsor","slug":"windsor","name":"Windsor","type":"mcp","url":"https://github.com/windsor-ai/windsor_mcp","page_url":"https://unfragile.ai/windsor","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-windsor__cap_0","uri":"capability://tool.use.integration.natural.language.business.data.querying.without.sql","name":"natural language business data querying without sql","description":"Translates natural language questions into optimized queries against Windsor's integrated data warehouse without requiring users to write SQL. Uses LLM-driven query generation with schema awareness to map user intent to appropriate data sources, handling multi-table joins and aggregations transparently. The MCP protocol bridges the LLM's function-calling interface with Windsor's query execution engine, enabling conversational data exploration across connected business systems.","intents":["Ask questions about my business metrics without knowing SQL syntax","Explore relationships between data from different business systems","Get quick answers about sales, marketing, or operational metrics","Drill down into data without writing custom queries"],"best_for":["Non-technical business users querying their own data","Data analysts reducing time spent on query writing","Product managers exploring metrics during decision-making","Teams with multiple data sources needing unified access"],"limitations":["Query complexity is limited by LLM's ability to understand schema — very large schemas (100+ tables) may require schema pruning or summarization","No support for custom window functions or complex recursive CTEs — limited to standard aggregations and joins","Query optimization relies on LLM reasoning; may generate inefficient queries for complex analytical patterns","Requires pre-integrated data sources in Windsor — cannot query arbitrary databases without prior setup"],"requires":["Windsor.ai account with at least one connected data source","MCP-compatible LLM client (Claude, custom LLM with MCP support)","Network access to Windsor API endpoints","API credentials for Windsor authentication"],"input_types":["natural language queries","conversational follow-ups with context"],"output_types":["structured data (JSON/CSV)","aggregated metrics","time-series data","natural language summaries"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-windsor__cap_1","uri":"capability://memory.knowledge.multi.source.data.integration.and.schema.discovery","name":"multi-source data integration and schema discovery","description":"Provides automatic schema discovery and normalization across Windsor's integrated data sources (CRM, marketing platforms, analytics tools, databases, etc.), exposing a unified schema to the LLM through MCP's resource listing interface. The capability handles schema mapping, field aliasing, and relationship inference without manual configuration, allowing the LLM to understand available data without users manually documenting table structures or relationships.","intents":["Understand what data is available across all my business systems","Discover relationships between data from different sources","Get the LLM to automatically understand my data structure","Explore new data sources without manual schema documentation"],"best_for":["Organizations with multiple disconnected data sources","Teams lacking dedicated data engineering resources","Rapid prototyping scenarios requiring quick data access","Non-technical users needing to explore unfamiliar datasets"],"limitations":["Schema discovery is limited to sources Windsor has pre-built connectors for — custom or proprietary data sources require manual integration","Relationship inference may be incomplete for complex schemas with implicit relationships not captured in foreign keys","Schema changes in source systems may not be immediately reflected — requires periodic refresh cycles","Large schemas (1000+ fields) may exceed LLM context windows, requiring schema summarization or filtering"],"requires":["Windsor.ai account with configured data source connectors","Read permissions on source systems (CRM, analytics platforms, databases)","MCP client with sufficient context window to handle schema information"],"input_types":["data source credentials","connection parameters"],"output_types":["schema definitions (tables, fields, types)","relationship mappings","field metadata and descriptions"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-windsor__cap_2","uri":"capability://data.processing.analysis.contextual.data.analysis.with.business.metric.interpretation","name":"contextual data analysis with business metric interpretation","description":"Executes queries against Windsor's data warehouse and automatically contextualizes results with business metric interpretation, trend analysis, and anomaly detection. The capability combines query execution with post-processing logic that compares results against historical baselines, calculates growth rates, identifies outliers, and generates business-relevant insights without requiring users to manually specify analysis parameters or thresholds.","intents":["Get insights about what the data means, not just raw numbers","Identify trends and anomalies in my business metrics","Understand how current performance compares to historical baselines","Receive actionable interpretations of query results"],"best_for":["Business analysts needing rapid insight generation","Executives requiring metric interpretation for decision-making","Teams using LLMs as analytical assistants","Organizations with mature data warehouses and historical data"],"limitations":["Anomaly detection relies on statistical baselines — requires sufficient historical data (typically 3+ months) to establish meaningful thresholds","Interpretation quality depends on LLM's domain knowledge — may miss industry-specific context or business rules","No support for causal analysis — can identify correlations but not explain root causes","Threshold-based anomaly detection may generate false positives in highly seasonal or volatile metrics"],"requires":["Historical data in Windsor warehouse (minimum 3 months for baseline calculation)","Query execution permissions on source data","LLM with sufficient reasoning capability for metric interpretation"],"input_types":["query results (structured data)","historical baseline data"],"output_types":["interpreted insights (natural language)","trend analysis","anomaly flags","comparative metrics"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-windsor__cap_3","uri":"capability://planning.reasoning.multi.step.analytical.workflows.with.data.persistence","name":"multi-step analytical workflows with data persistence","description":"Enables LLMs to construct multi-step analytical workflows where intermediate query results are persisted and referenced in subsequent queries, supporting complex analysis patterns like cohort analysis, funnel analysis, and comparative metrics. The capability manages result caching and state across multiple MCP function calls, allowing the LLM to build sophisticated analyses without recomputing intermediate steps or losing context between queries.","intents":["Build complex analyses that require multiple sequential queries","Perform cohort analysis comparing user segments across time periods","Analyze conversion funnels with multiple filtering steps","Compare metrics across different data slices without recomputing"],"best_for":["Data analysts performing exploratory analysis","Teams building custom analytical workflows","Organizations analyzing user behavior and conversion funnels","Scenarios requiring iterative refinement of data subsets"],"limitations":["Result persistence is session-scoped — intermediate results are lost after conversation ends; requires external storage for long-term persistence","No built-in versioning or audit trail for intermediate results — difficult to track how final results were derived","Memory overhead increases with number of intermediate results — very large result sets may exceed context window limits","No automatic query optimization across workflow steps — sequential queries may be inefficient compared to single optimized query"],"requires":["MCP client with session state management","Sufficient context window to store intermediate results","Query execution permissions for all workflow steps"],"input_types":["sequential natural language queries","references to previous query results"],"output_types":["intermediate result sets","final analytical results","workflow execution logs"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-windsor__cap_4","uri":"capability://automation.workflow.real.time.data.synchronization.and.freshness.management","name":"real-time data synchronization and freshness management","description":"Maintains synchronized copies of business data from source systems with configurable refresh intervals and freshness guarantees. The capability handles incremental syncs, change detection, and conflict resolution across multiple sources, exposing data freshness metadata to the LLM so it can make informed decisions about data reliability and whether to refresh before querying. Uses MCP's resource metadata to communicate sync status and last-update timestamps.","intents":["Query data with confidence about how recent it is","Ensure I'm analyzing current data, not stale snapshots","Understand which data sources are up-to-date","Make decisions about when to refresh data before analysis"],"best_for":["Organizations requiring near-real-time data access","Teams analyzing fast-moving metrics (sales, traffic, conversions)","Scenarios where data staleness impacts decision quality","Compliance-sensitive use cases requiring audit trails"],"limitations":["Sync latency depends on source system API rate limits — real-time sync not possible for all sources, typically 5-60 minute delays","Incremental sync reliability depends on source system's change tracking capabilities — some sources only support full refreshes","Conflict resolution is automatic but may not match business logic — manual intervention required for complex reconciliation","Freshness guarantees are best-effort — network failures or source system outages may delay syncs without notification"],"requires":["Windsor.ai account with configured sync schedules","Source system API access with appropriate rate limits","Network connectivity to source systems and Windsor infrastructure"],"input_types":["sync configuration (refresh intervals, incremental vs full)","source system credentials"],"output_types":["freshness metadata (last sync time, sync status)","data with timestamp annotations","sync error logs"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-windsor__cap_5","uri":"capability://safety.moderation.access.control.and.data.governance.through.llm.context","name":"access control and data governance through llm context","description":"Enforces row-level and column-level access controls at query execution time, ensuring LLMs only access data they're authorized to query. The capability integrates with Windsor's permission model to filter query results based on user context, data classification, and compliance rules, preventing unauthorized data exposure while maintaining transparent access patterns that the LLM can understand and reason about.","intents":["Ensure the LLM only accesses data I'm authorized to share","Maintain data governance and compliance requirements","Prevent accidental exposure of sensitive data through LLM queries","Understand what data the LLM can and cannot access"],"best_for":["Organizations with strict data governance requirements","Multi-tenant SaaS platforms using LLMs for customer analytics","Compliance-sensitive industries (healthcare, finance, legal)","Teams sharing LLM access across multiple users with different permissions"],"limitations":["Access control is enforced at query execution time — no pre-filtering of schema, so LLM can see table names even if it lacks access to rows","Row-level security filtering may significantly impact query performance for large datasets — requires careful index design","Complex permission rules may be difficult for LLM to reason about — may generate queries that fail due to insufficient permissions","No built-in audit logging of LLM data access — requires external logging for compliance reporting"],"requires":["Windsor.ai account with configured access control policies","User authentication and authorization context","LLM client capable of handling permission-denied errors gracefully"],"input_types":["user context (identity, roles, permissions)","data classification metadata"],"output_types":["filtered query results","permission-denied errors","access control metadata"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-windsor__cap_6","uri":"capability://automation.workflow.batch.query.execution.and.result.export","name":"batch query execution and result export","description":"Supports execution of multiple queries in batch mode with results exported to various formats (CSV, JSON, Parquet) and destinations (cloud storage, email, webhooks). The capability handles query queuing, parallel execution where possible, and result aggregation, enabling LLMs to request bulk data exports or schedule recurring analytical reports without blocking on individual query execution.","intents":["Export large datasets for external analysis or sharing","Generate recurring reports and schedule them for delivery","Execute multiple queries in parallel without waiting for each result","Save query results to cloud storage for downstream processing"],"best_for":["Teams generating regular analytical reports","Data scientists exporting data for model training","Organizations sharing data with external partners","Scenarios requiring large-scale data extraction"],"limitations":["Batch execution is asynchronous — LLM cannot directly access results within conversation, requires polling or webhook callbacks","Export formats are limited to CSV, JSON, Parquet — no support for specialized formats (Excel, PDF with formatting)","Large result sets may exceed storage quotas or timeout during export — requires pagination or streaming for very large datasets","No built-in data transformation during export — requires post-processing for format conversion or field mapping"],"requires":["Query execution permissions","Storage credentials for export destination (S3, GCS, etc.)","Sufficient quota for result storage and export"],"input_types":["multiple query specifications","export format and destination configuration"],"output_types":["exported files (CSV, JSON, Parquet)","export status and metadata","webhook notifications"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-windsor__cap_7","uri":"capability://data.processing.analysis.caching.and.query.optimization.with.execution.plan.visibility","name":"caching and query optimization with execution plan visibility","description":"Implements intelligent query result caching with cache invalidation based on source data freshness, and exposes query execution plans to the LLM so it can understand performance characteristics and optimize queries. The capability tracks which source tables are referenced in each query, automatically invalidates cached results when those tables are updated, and provides execution time estimates to help the LLM make decisions about query complexity.","intents":["Avoid re-executing expensive queries when data hasn't changed","Understand why a query is slow and how to optimize it","Get query execution time estimates before running expensive analyses","Reuse query results across multiple analytical steps"],"best_for":["Teams running repeated analytical queries","Scenarios with expensive queries over large datasets","Organizations optimizing for query cost and latency","Data analysts needing visibility into query performance"],"limitations":["Cache invalidation is table-level — cannot distinguish between rows that changed vs entire table refresh, may invalidate more cache than necessary","Execution plan visibility is limited to Windsor's query engine — cannot optimize queries against external data sources","Cache TTL is fixed — no support for custom cache policies based on data volatility or business requirements","Query optimization recommendations are heuristic-based — may not account for complex business logic or data distribution"],"requires":["Query execution permissions","Access to execution plan metadata","Sufficient cache storage for result sets"],"input_types":["query specifications","cache configuration"],"output_types":["cached or fresh query results","execution plans","performance metrics (execution time, rows scanned)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Windsor.ai account with at least one connected data source","MCP-compatible LLM client (Claude, custom LLM with MCP support)","Network access to Windsor API endpoints","API credentials for Windsor authentication","Windsor.ai account with configured data source connectors","Read permissions on source systems (CRM, analytics platforms, databases)","MCP client with sufficient context window to handle schema information","Historical data in Windsor warehouse (minimum 3 months for baseline calculation)","Query execution permissions on source data","LLM with sufficient reasoning capability for metric interpretation"],"failure_modes":["Query complexity is limited by LLM's ability to understand schema — very large schemas (100+ tables) may require schema pruning or summarization","No support for custom window functions or complex recursive CTEs — limited to standard aggregations and joins","Query optimization relies on LLM reasoning; may generate inefficient queries for complex analytical patterns","Requires pre-integrated data sources in Windsor — cannot query arbitrary databases without prior setup","Schema discovery is limited to sources Windsor has pre-built connectors for — custom or proprietary data sources require manual integration","Relationship inference may be incomplete for complex schemas with implicit relationships not captured in foreign keys","Schema changes in source systems may not be immediately reflected — requires periodic refresh cycles","Large schemas (1000+ fields) may exceed LLM context windows, requiring schema summarization or filtering","Anomaly detection relies on statistical baselines — requires sufficient historical data (typically 3+ months) to establish meaningful thresholds","Interpretation quality depends on LLM's domain knowledge — may miss industry-specific context or business rules","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.39999999999999997,"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":"2026-06-17T09:51:04.689Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=windsor","compare_url":"https://unfragile.ai/compare?artifact=windsor"}},"signature":"3D9pBnhQAELsPQjtYBkqxYv3BYWXFwewKaEJkLScHFT1pW5ByCo42glwwB1MTSFHllALLYprxVEIfqONmcZtBg==","signedAt":"2026-06-20T23:42:27.819Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/windsor","artifact":"https://unfragile.ai/windsor","verify":"https://unfragile.ai/api/v1/verify?slug=windsor","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"}}