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Implements the MCP server specification to translate ORM operations into protocol-compliant request/response handlers, enabling frontend applications and AI agents to query and manipulate data without direct database access.","intents":["I want to expose my functional-models ORM to Claude or other MCP-compatible LLM clients without building a custom REST API","I need my frontend AI agent to safely query and update database records through a standardized protocol","I want to provide LLM-based applications with structured access to my data models without exposing raw database credentials"],"best_for":["Frontend developers building LLM-integrated applications with functional-models ORM","Teams using Model Context Protocol for AI agent orchestration","Startups prototyping AI-driven CRUD interfaces without backend infrastructure"],"limitations":["Requires functional-models ORM as the underlying data layer — no support for other ORMs or raw SQL","MCP protocol overhead adds latency compared to direct ORM method calls; typical ~50-150ms per request","No built-in authentication/authorization — relies on MCP client-side security and transport layer (e.g., stdio, SSE) for access control","Limited to operations supported by functional-models API; complex stored procedures or database-specific features not exposed"],"requires":["functional-models ORM instance (any recent version with standard model definitions)","@modelcontextprotocol/sdk (MCP SDK for Node.js)","Node.js 16+ runtime","MCP-compatible client (Claude, custom LLM agent, or MCP inspector)"],"input_types":["MCP resource requests (JSON-RPC 2.0 format)","MCP tool call payloads with model names and operation parameters","Query filters and sort specifications as JSON objects"],"output_types":["MCP resource responses (model instances as JSON)","Tool execution results with operation status and affected records","Structured error responses with functional-models validation messages"],"categories":["tool-use-integration","orm-protocol-bridge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-functional-models-orm-mcp__cap_1","uri":"capability://tool.use.integration.functional.models.model.to.mcp.resource.mapping","name":"functional-models model-to-mcp resource mapping","description":"Automatically maps functional-models ORM model definitions (entities, fields, relationships) to MCP resource endpoints, allowing LLM clients to discover and fetch model instances as structured resources. Uses reflection or schema introspection on functional-models models to generate MCP resource URIs and content types, enabling semantic understanding of data structure without manual configuration.","intents":["I want Claude to understand the structure of my database models without me manually documenting them","I need my LLM agent to discover available models and their fields dynamically at runtime","I want to expose model instances as first-class MCP resources that LLMs can reference and retrieve"],"best_for":["Developers using functional-models with strongly-typed model definitions","Teams building self-documenting AI data interfaces","Applications requiring dynamic model discovery for multi-tenant or plugin-based architectures"],"limitations":["Mapping quality depends on functional-models schema completeness — custom field types or undocumented properties may not translate cleanly to MCP schemas","No support for computed fields or virtual properties unless explicitly registered in functional-models","Relationship traversal depth is limited to prevent circular references and excessive resource generation"],"requires":["functional-models models with explicit field definitions and type annotations","MCP SDK resource handler implementation","Node.js 16+"],"input_types":["functional-models model class definitions","Model instance IDs or query parameters for resource retrieval"],"output_types":["MCP resource URIs (e.g., 'models://User/123')","JSON representations of model instances with field metadata","Schema descriptions for LLM consumption"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-functional-models-orm-mcp__cap_2","uri":"capability://tool.use.integration.mcp.tool.based.crud.operation.dispatch","name":"mcp tool-based crud operation dispatch","description":"Exposes functional-models ORM CRUD operations (create, read, update, delete, query) as MCP tools with schema-validated parameters. Translates MCP tool call requests into functional-models method invocations, handles validation errors, and returns results in MCP tool result format. Implements parameter marshaling to convert JSON tool arguments into ORM-compatible types (e.g., nested objects for relationships).","intents":["I want my LLM agent to create, update, or delete records in my database through MCP tool calls","I need schema validation on tool parameters before they reach the ORM to prevent invalid operations","I want to expose filtered queries and complex filters to LLMs in a safe, controlled manner"],"best_for":["Developers building LLM agents that need persistent data mutation capabilities","Teams requiring audit trails of AI-driven database changes (MCP tool calls are loggable)","Applications with complex validation rules that should be enforced before ORM execution"],"limitations":["Tool parameter schemas are generated from functional-models definitions; custom validation logic not in the model definition may not be exposed to the LLM","Batch operations (bulk insert/update) may not be fully supported depending on functional-models API surface","Transaction support depends on underlying database and functional-models transaction API; no built-in distributed transaction handling","Tool result payloads are JSON-serialized; large result sets (>10MB) may exceed MCP message size limits"],"requires":["functional-models ORM with CRUD method implementations","MCP SDK tool handler registration","Node.js 16+","Functional-models model definitions with field validation rules"],"input_types":["MCP tool call JSON with operation type (create/read/update/delete/query) and parameters","Query filters as JSON objects matching functional-models filter syntax","Model instance data as JSON objects"],"output_types":["MCP tool results with created/updated/deleted record data","Query result sets as JSON arrays","Error responses with validation messages from functional-models"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-functional-models-orm-mcp__cap_3","uri":"capability://automation.workflow.frontend.compatible.mcp.server.lifecycle.management","name":"frontend-compatible mcp server lifecycle management","description":"Provides server initialization, connection handling, and lifecycle hooks optimized for frontend environments (browser or Electron). Implements MCP server protocol with support for stdio, WebSocket, or Server-Sent Events (SSE) transports, allowing frontend applications to spawn and communicate with the ORM datastore provider without a separate backend process. Handles graceful shutdown, error recovery, and connection state management.","intents":["I want to run my ORM datastore provider in a frontend environment (Electron, Node.js worker, or browser-based runtime)","I need my frontend app to establish a persistent connection to the MCP server without managing separate processes","I want automatic reconnection and error handling when the MCP server connection drops"],"best_for":["Desktop application developers using Electron with local databases","Frontend teams building AI-integrated tools with embedded Node.js runtimes","Developers prototyping full-stack AI applications without separate backend infrastructure"],"limitations":["Stdio transport requires the MCP server and client to run in the same process or via spawned child process; not suitable for remote deployments","WebSocket/SSE transports require additional infrastructure (proxy, reverse proxy) if deployed behind firewalls or load balancers","No built-in clustering or horizontal scaling; each frontend instance runs its own MCP server instance","Database file locking may occur if multiple frontend instances try to access the same functional-models datastore simultaneously"],"requires":["Node.js 16+ runtime available in frontend environment (Electron, Node.js worker, or equivalent)","@modelcontextprotocol/sdk with transport support","functional-models ORM instance with database connection","MCP-compatible client library (e.g., @modelcontextprotocol/sdk client)"],"input_types":["MCP server configuration (transport type, port, database path)","Functional-models datastore instance"],"output_types":["MCP server instance ready for client connections","Connection status events and lifecycle callbacks"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-functional-models-orm-mcp__cap_4","uri":"capability://data.processing.analysis.query.filter.translation.and.execution","name":"query filter translation and execution","description":"Translates MCP tool call filter parameters (JSON objects) into functional-models query syntax, executes filtered queries against the ORM, and returns paginated or limited result sets. Supports common filter operators (equals, contains, range, logical AND/OR) and translates them to functional-models filter API calls. Implements result pagination to prevent memory exhaustion from large queries.","intents":["I want my LLM agent to query my database with complex filters without writing raw SQL","I need to safely limit query result sizes to prevent the LLM from receiving gigabytes of data","I want to support natural language filter descriptions that are translated to structured queries"],"best_for":["Developers building search or filtering features in LLM-driven applications","Teams needing to expose database queries to LLMs with safety guardrails (result limits, timeout protection)","Applications with complex filtering requirements that benefit from ORM abstraction"],"limitations":["Filter operator support is limited to what functional-models ORM provides; database-specific operators (e.g., PostgreSQL JSONB operators) may not be available","Pagination is implemented at the ORM level, not the database level; large offsets may be slow","No support for aggregation queries (COUNT, SUM, AVG) unless functional-models provides explicit methods","Full-text search requires functional-models integration with database FTS capabilities; not guaranteed to be available"],"requires":["functional-models ORM with query/filter API","MCP tool parameter schema supporting filter object structures","Node.js 16+"],"input_types":["Filter objects as JSON (e.g., {field: 'name', operator: 'contains', value: 'John'})","Pagination parameters (limit, offset or cursor)","Sort specifications (field, direction)"],"output_types":["Paginated result sets as JSON arrays","Result metadata (total count, has_more, cursor for next page)","Error messages if filter syntax is invalid"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-functional-models-orm-mcp__cap_5","uri":"capability://data.processing.analysis.relationship.traversal.and.nested.data.loading","name":"relationship traversal and nested data loading","description":"Handles functional-models relationship definitions (one-to-many, many-to-many, foreign keys) and exposes them through MCP resources and tools. When an LLM requests a model instance, automatically loads or provides access to related records. Implements lazy loading or eager loading strategies to balance performance and data completeness, preventing N+1 query problems through relationship batching.","intents":["I want my LLM agent to understand and traverse relationships between models (e.g., User -> Orders -> Items)","I need to expose related records without the LLM making multiple separate queries","I want to prevent N+1 query problems when the LLM requests deeply nested data"],"best_for":["Applications with complex relational data models (e.g., e-commerce, CRM, multi-tenant systems)","Teams building LLM agents that need to understand data relationships for reasoning","Developers optimizing database query performance for AI-driven applications"],"limitations":["Relationship loading strategy (lazy vs eager) is fixed at configuration time; no per-query control","Circular relationships (A -> B -> A) require special handling to prevent infinite recursion; depth limits are enforced","Many-to-many relationships require explicit join table definitions in functional-models; implicit relationships not supported","Relationship filtering (e.g., 'load only active orders') requires functional-models support for relationship filters"],"requires":["functional-models models with explicit relationship definitions (foreign keys, associations)","MCP SDK resource and tool handlers","Node.js 16+"],"input_types":["Model instance IDs or queries","Relationship traversal paths (e.g., 'User.Orders.Items')","Relationship filter parameters"],"output_types":["Nested JSON objects with related records included","MCP resources representing related model instances","Relationship metadata (cardinality, foreign key fields)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-functional-models-orm-mcp__cap_6","uri":"capability://safety.moderation.error.handling.and.validation.feedback","name":"error handling and validation feedback","description":"Captures functional-models validation errors, ORM operation failures, and database errors, translating them into MCP-compatible error responses with actionable feedback for LLM clients. Implements error categorization (validation, constraint violation, not found, permission denied) and provides structured error messages that LLMs can parse and act upon. Prevents sensitive database error details from leaking to clients.","intents":["I want my LLM agent to understand why an operation failed and retry or adjust its approach","I need to prevent database errors from exposing sensitive information to the LLM","I want validation errors to be clear enough that the LLM can self-correct"],"best_for":["Teams building resilient LLM agents that can handle and recover from errors","Applications requiring audit trails of failed operations","Developers prioritizing security by preventing information leakage through error messages"],"limitations":["Error categorization is limited to common patterns; unusual database errors may be classified as generic 'unknown' errors","Validation error messages depend on functional-models error message quality; custom validation logic may produce unclear messages","No built-in retry logic; LLM clients must implement their own retry strategies","Error context (e.g., which field failed validation) may be lost if functional-models doesn't provide detailed error information"],"requires":["functional-models ORM with validation and error reporting","MCP error response format implementation","Node.js 16+"],"input_types":["Exceptions and errors from functional-models ORM","Database driver errors"],"output_types":["MCP error responses with error code, message, and optional details","Structured validation error objects with field-level feedback","Categorized error types for LLM decision-making"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["functional-models ORM instance (any recent version with standard model definitions)","@modelcontextprotocol/sdk (MCP SDK for Node.js)","Node.js 16+ runtime","MCP-compatible client (Claude, custom LLM agent, or MCP inspector)","functional-models models with explicit field definitions and type annotations","MCP SDK resource handler implementation","Node.js 16+","functional-models ORM with CRUD method implementations","MCP SDK tool handler registration","Functional-models model definitions with field validation rules"],"failure_modes":["Requires functional-models ORM as the underlying data layer — no support for other ORMs or raw SQL","MCP protocol overhead adds latency compared to direct ORM method calls; typical ~50-150ms per request","No built-in authentication/authorization — relies on MCP client-side security and transport layer (e.g., stdio, SSE) for access control","Limited to operations supported by functional-models API; complex stored procedures or database-specific features not exposed","Mapping quality depends on functional-models schema completeness — custom field types or undocumented properties may not translate cleanly to MCP schemas","No support for computed fields or virtual properties unless explicitly registered in functional-models","Relationship traversal depth is limited to prevent circular references and excessive resource generation","Tool parameter schemas are generated from functional-models definitions; custom validation logic not in the model definition may not be exposed to the LLM","Batch operations (bulk insert/update) may not be fully supported depending on functional-models API surface","Transaction support depends on underlying database and functional-models transaction API; no built-in distributed transaction handling","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.39,"ecosystem":0.42,"match_graph":0.25,"freshness":0.52,"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":"2026-05-24T12:16:23.903Z","last_scraped_at":"2026-05-03T14:23:44.895Z","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-functional-models-orm-mcp","compare_url":"https://unfragile.ai/compare?artifact=npm-functional-models-orm-mcp"}},"signature":"Np50H3fFz7OnndskdgCe1qB9SNOdfT3GNNoo6HS7251SVeFJsplT/PCzjXjfx9CiFnjsS7VIyg1MHTY5UrUDDw==","signedAt":"2026-06-21T13:19:07.695Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-functional-models-orm-mcp","artifact":"https://unfragile.ai/npm-functional-models-orm-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=npm-functional-models-orm-mcp","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"}}