{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-schemaflow","slug":"schemaflow","name":"SchemaFlow","type":"mcp","url":"https://github.com/CryptoRadi/schemaflow-mcp-server","page_url":"https://unfragile.ai/schemaflow","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-schemaflow__cap_0","uri":"capability://safety.moderation.token.based.schema.access.without.database.credentials","name":"token-based schema access without database credentials","description":"SchemaFlow implements a credential-isolation architecture where AI-IDEs authenticate via time-limited MCP tokens rather than direct database credentials. The server maintains cached schema metadata separately from the database layer, and token validation occurs at the SSE gateway before any schema data is transmitted. This eliminates the need for AI-IDEs to store or transmit production database passwords, reducing attack surface and audit complexity.","intents":["I want my AI-IDE to access database schema without exposing production credentials to my local machine","I need to grant multiple developers schema access without sharing database passwords","I want to revoke schema access to a developer without rotating database credentials"],"best_for":["teams with strict credential management policies","enterprises requiring audit trails for schema access","developers working with production databases in local environments"],"limitations":["Schema metadata is cached and may lag behind live database changes until manually refreshed via dashboard","Token revocation is not instantaneous — cached tokens remain valid until expiration","No fine-grained column-level access control — tokens grant access to entire cached schema"],"requires":["PostgreSQL 12+ or Supabase account","Node.js 16+ for running SchemaFlow server","HTTPS connection to api.schemaflow.dev/mcp/ (no HTTP fallback)","Valid MCP token generated from SchemaFlow dashboard"],"input_types":["MCP token (string)","database connection string (initial setup only, not stored in IDE)"],"output_types":["schema metadata (JSON)","validation results (JSON)"],"categories":["safety-moderation","security"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-schemaflow__cap_1","uri":"capability://memory.knowledge.real.time.schema.caching.with.manual.refresh.synchronization","name":"real-time schema caching with manual refresh synchronization","description":"SchemaFlow maintains an in-memory or persistent cache of PostgreSQL/Supabase schema metadata that is populated during initial database connection and updated when users trigger a refresh via the web dashboard. The caching strategy stores table definitions, column metadata, constraints, indexes, and relationships without requiring continuous polling of the live database. Cache invalidation is explicit (user-initiated) rather than time-based, ensuring schema consistency across all connected AI-IDEs while minimizing database load.","intents":["I want my AI-IDE to have instant access to schema metadata without querying the database on every request","I need to update schema context in my AI-IDE after running migrations","I want to reduce database load from repeated schema introspection queries"],"best_for":["teams with stable schema that changes infrequently","development workflows where schema updates are coordinated","scenarios with high-latency or rate-limited database connections"],"limitations":["Schema changes are not automatically detected — manual refresh required via dashboard","Cache consistency depends on user discipline; stale schema can cause code generation errors","No incremental sync — full schema is re-cached on refresh, not delta updates","Cache size grows linearly with schema complexity; very large schemas (1000+ tables) may impact performance"],"requires":["Initial database connection to populate cache","Access to SchemaFlow dashboard to trigger refresh","Network connectivity to api.schemaflow.dev for cache updates"],"input_types":["PostgreSQL/Supabase connection string (one-time during setup)","user-initiated refresh trigger (via dashboard)"],"output_types":["cached schema metadata (JSON)","cache status/timestamp (JSON)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-schemaflow__cap_10","uri":"capability://automation.workflow.schema.change.detection.and.cache.invalidation.workflow","name":"schema change detection and cache invalidation workflow","description":"SchemaFlow implements a manual schema refresh workflow where users trigger cache updates via the web dashboard after running database migrations. The refresh process re-executes schema introspection queries against the live database, updates the cached metadata, and notifies all connected AI-IDEs of the schema change. The workflow is explicit (user-initiated) rather than automatic, ensuring schema consistency across all IDEs and preventing stale data issues.","intents":["I want to update my cached schema in SchemaFlow after running a migration","I need to notify all connected AI-IDEs that the database schema has changed","I want to ensure all developers are using the latest schema version"],"best_for":["teams with coordinated schema changes (migrations run at specific times)","development workflows where schema updates are infrequent","scenarios where stale schema would cause significant issues"],"limitations":["Schema changes are not automatically detected — manual refresh required","Refresh is blocking — all connected IDEs receive updated schema simultaneously","No incremental sync — full schema is re-cached, not delta updates","No rollback mechanism — previous schema versions are not retained","Refresh latency depends on schema size and database load"],"requires":["Access to SchemaFlow dashboard","Database connectivity from SchemaFlow server","All connected IDEs must be online to receive updated schema"],"input_types":["user action (refresh button in dashboard)"],"output_types":["updated schema cache (JSON)","notification to connected IDEs (SSE event)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-schemaflow__cap_11","uri":"capability://safety.moderation.secure.https.communication.with.token.based.authentication","name":"secure https communication with token-based authentication","description":"SchemaFlow enforces HTTPS-only communication between AI-IDEs and the MCP server, with token-based authentication validated at the SSE gateway before any schema data is transmitted. The implementation uses standard HTTPS with TLS encryption, and tokens are validated on every request using cryptographic verification. No unencrypted HTTP connections are allowed, and tokens are never logged or exposed in error messages.","intents":["I want to ensure my database schema is transmitted securely over the network","I need to verify that only authorized IDEs can access my schema","I want to prevent man-in-the-middle attacks on schema data"],"best_for":["teams with security-sensitive databases","regulated environments requiring encryption","scenarios where schema data is considered sensitive"],"limitations":["HTTPS adds ~50-100ms latency per request compared to HTTP","Token validation adds ~10-20ms per request","No certificate pinning — vulnerable to compromised CAs","Token is transmitted in request headers, vulnerable to header sniffing if HTTPS is compromised","No end-to-end encryption — SchemaFlow server can read schema data"],"requires":["HTTPS connection to api.schemaflow.dev/mcp/","Valid TLS certificate (automatically verified by IDE)","Valid MCP token"],"input_types":["HTTPS request with token in header"],"output_types":["encrypted HTTPS response with schema data"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-schemaflow__cap_2","uri":"capability://tool.use.integration.mcp.tool.invocation.for.schema.retrieval.and.analysis","name":"mcp tool invocation for schema retrieval and analysis","description":"SchemaFlow exposes three MCP-compliant tools (get_schema, analyze_database, check_schema_alignment) that AI-IDEs invoke through the Model Context Protocol. These tools are registered with the MCP server and callable by AI assistants during conversation, returning structured schema metadata, analysis results, and validation reports. The implementation uses SSE (Server-Sent Events) over HTTPS for bidirectional communication, allowing AI-IDEs to request schema data and receive results without polling.","intents":["I want my AI assistant to retrieve table and column definitions during code generation","I need the AI to analyze my database structure for design issues or optimization opportunities","I want the AI to validate my schema against best practices (naming conventions, indexing, constraints)"],"best_for":["AI-IDE developers integrating with Cursor, Windsurf, or VS Code + Cline","teams using AI assistants for database-aware code generation","developers who want AI-driven schema analysis without manual introspection"],"limitations":["Tool invocation is asynchronous and depends on AI model's ability to recognize when to call tools","No streaming responses — full schema data is buffered and sent in single SSE message","Tool output is limited to what the AI model can process in context; very large schemas may exceed token limits","No custom tool registration — only three built-in tools are available"],"requires":["AI-IDE with MCP support (Cursor 0.30+, Windsurf, VS Code + Cline extension)","Valid MCP token for authentication","HTTPS connection to api.schemaflow.dev/mcp/","Schema already cached in SchemaFlow backend"],"input_types":["MCP tool request (JSON with tool name and parameters)","optional query parameters (table name, analysis type)"],"output_types":["schema metadata (JSON with tables, columns, constraints, indexes)","analysis report (JSON with findings and recommendations)","alignment report (JSON with validation results)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-schemaflow__cap_3","uri":"capability://tool.use.integration.get.schema.tool.for.targeted.schema.metadata.retrieval","name":"get_schema tool for targeted schema metadata retrieval","description":"The get_schema MCP tool retrieves filtered schema metadata from the cache, accepting optional parameters to target specific tables or return full database structure. It returns structured JSON containing table definitions, column metadata (name, type, nullable, default), constraints (primary key, foreign key, unique), and indexes. The tool implements parameter validation and error handling for missing tables, returning clear error messages when requested schema elements don't exist.","intents":["I want the AI to fetch the structure of a specific table to generate accurate INSERT/UPDATE queries","I need the AI to retrieve all tables in the database to understand the data model","I want to get column definitions including types and constraints for a particular table"],"best_for":["AI-assisted SQL query generation","database schema exploration during development","code generation that requires accurate table/column names and types"],"limitations":["Returns cached schema only — does not reflect live database changes until refresh","No pagination — returns entire table structure even for tables with 100+ columns","No column-level filtering — returns all columns or none for a given table","Does not return table statistics (row count, size) or performance metrics"],"requires":["Valid MCP token","Schema already cached in SchemaFlow backend","Table name must exist in cached schema"],"input_types":["optional table_name parameter (string)","optional full_schema parameter (boolean to return all tables)"],"output_types":["schema metadata (JSON object with tables array containing columns, constraints, indexes)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-schemaflow__cap_4","uri":"capability://planning.reasoning.analyze.database.tool.for.schema.design.assessment","name":"analyze_database tool for schema design assessment","description":"The analyze_database MCP tool performs static analysis on cached schema metadata to identify design issues, optimization opportunities, and best practice violations. It examines table structures, constraint definitions, index coverage, and naming conventions, returning a structured report with findings categorized by severity (error, warning, info). The analysis runs entirely on cached data without querying the live database, making it fast and suitable for real-time AI-assisted feedback.","intents":["I want the AI to identify missing indexes that could improve query performance","I need the AI to flag tables without primary keys or proper constraints","I want the AI to suggest naming convention improvements for my schema"],"best_for":["schema design review during development","AI-assisted database optimization","teams establishing schema best practices"],"limitations":["Analysis is based on structure only — does not examine actual query patterns or performance metrics","No access to live database statistics (row counts, index usage) so recommendations may not reflect real-world impact","Cannot detect application-level issues (N+1 queries, missing denormalization) only schema structure problems","Recommendations are generic best practices, not tailored to specific workload patterns"],"requires":["Valid MCP token","Schema already cached in SchemaFlow backend","Sufficient schema complexity to generate meaningful analysis"],"input_types":["optional analysis_type parameter (string: 'full', 'performance', 'constraints', 'naming')"],"output_types":["analysis report (JSON with findings array, each containing type, severity, table, description, recommendation)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-schemaflow__cap_5","uri":"capability://safety.moderation.check.schema.alignment.tool.for.best.practice.validation","name":"check_schema_alignment tool for best practice validation","description":"The check_schema_alignment MCP tool validates cached schema against a set of configurable best practices and standards, returning a compliance report. It checks for naming conventions (snake_case vs camelCase), constraint coverage (all tables have primary keys), index presence (foreign keys are indexed), and other structural patterns. The tool returns a structured report indicating which standards are met, which are violated, and severity of violations, enabling AI-assisted schema remediation.","intents":["I want the AI to verify my schema follows our team's naming conventions","I need to check if all tables have proper primary keys and constraints","I want the AI to identify tables missing indexes on foreign keys"],"best_for":["enforcing schema standards across team projects","schema migration validation","compliance checking for regulated databases"],"limitations":["Alignment rules are predefined and not customizable per team or project","Cannot validate against application-specific constraints (e.g., 'all user tables must have created_at timestamp')","No severity weighting — all violations reported equally regardless of impact","Does not check data-level constraints (e.g., values in specific columns)"],"requires":["Valid MCP token","Schema already cached in SchemaFlow backend","Predefined alignment rules configured in SchemaFlow backend"],"input_types":["optional standards parameter (string array: 'naming', 'constraints', 'indexes', 'all')"],"output_types":["alignment report (JSON with standards array, each containing name, status, violations array with table, issue, severity)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-schemaflow__cap_6","uri":"capability://tool.use.integration.sse.based.bidirectional.communication.with.ai.ides","name":"sse-based bidirectional communication with ai-ides","description":"SchemaFlow uses Server-Sent Events (SSE) over HTTPS for real-time, bidirectional communication between the MCP server and AI-IDEs. The SSE connection is established after token authentication and remains open for the duration of the IDE session, allowing the server to push schema data and analysis results to the IDE without polling. The implementation handles connection lifecycle (establishment, heartbeat, reconnection) and message serialization/deserialization for MCP protocol compliance.","intents":["I want my AI-IDE to receive schema data in real-time without polling the server","I need reliable message delivery for MCP tool responses","I want the connection to automatically reconnect if the network drops"],"best_for":["AI-IDEs requiring low-latency schema access","environments with unreliable network connectivity","scenarios where polling would create excessive server load"],"limitations":["SSE is unidirectional by design (server-to-client); IDE-to-server requests still use HTTP POST","No built-in compression — large schema payloads consume significant bandwidth","Connection state is not persisted — reconnection requires re-authentication","SSE has browser compatibility limitations (not applicable for IDE plugins but relevant for web-based IDEs)","No message ordering guarantees if multiple tools are invoked simultaneously"],"requires":["HTTPS connection (no HTTP fallback)","AI-IDE with SSE support","Valid MCP token for initial authentication","Network connectivity to api.schemaflow.dev"],"input_types":["MCP token (for initial SSE handshake)","HTTP POST requests for tool invocations"],"output_types":["SSE events (JSON-formatted MCP messages)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-schemaflow__cap_7","uri":"capability://data.processing.analysis.postgresql.and.supabase.database.connection.with.schema.introspection","name":"postgresql and supabase database connection with schema introspection","description":"SchemaFlow connects to PostgreSQL and Supabase databases using standard database drivers, executing system catalog queries (pg_tables, pg_columns, pg_constraints, pg_indexes) to introspect schema structure. The connection is established once during setup and used to populate the initial schema cache; the connection is not maintained for every IDE request. The introspection process extracts table definitions, column metadata, constraints, indexes, and relationships, storing them in the cache for subsequent access.","intents":["I want to connect my PostgreSQL or Supabase database to SchemaFlow","I need SchemaFlow to automatically discover all tables, columns, and constraints in my database","I want to update my cached schema after running migrations"],"best_for":["PostgreSQL 12+ and Supabase users","teams with stable database schemas","development workflows where schema changes are coordinated"],"limitations":["Only PostgreSQL and Supabase are supported; no MySQL, SQL Server, or other databases","Connection requires valid credentials with schema introspection permissions (SELECT on information_schema)","Introspection is not incremental — full schema is re-scanned on refresh, not delta updates","Does not capture application-level metadata (e.g., table purpose, column descriptions) unless stored in PostgreSQL comments","Connection credentials must be provided during setup; no automatic credential rotation"],"requires":["PostgreSQL 12+ or Supabase account","Valid database connection string (host, port, database, user, password)","Database user with SELECT permissions on information_schema","Network connectivity from SchemaFlow server to database (for initial setup only)"],"input_types":["database connection string (postgresql://user:password@host:port/database)"],"output_types":["schema metadata (JSON with tables, columns, constraints, indexes)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-schemaflow__cap_8","uri":"capability://safety.moderation.mcp.token.generation.and.lifecycle.management","name":"mcp token generation and lifecycle management","description":"SchemaFlow provides a dashboard interface for generating time-limited MCP tokens that authenticate AI-IDE connections. Tokens are generated per database connection and can be revoked or regenerated via the dashboard. The token generation process creates a cryptographically secure token with configurable expiration (default unclear from docs but typical is 24-90 days), and token validation occurs at the SSE gateway before any schema data is transmitted. Token metadata (creation date, last used, expiration) is tracked for audit purposes.","intents":["I want to generate an authentication token for my AI-IDE to access my database schema","I need to revoke schema access for a developer without changing database credentials","I want to track which developers have accessed my schema and when"],"best_for":["teams requiring credential management and access control","scenarios where developer access needs to be revoked quickly","audit-sensitive environments requiring access tracking"],"limitations":["Token expiration is not configurable — uses fixed default duration","No fine-grained permissions — tokens grant access to entire cached schema, not specific tables","Token revocation is not instantaneous — cached tokens remain valid until expiration","No token rotation automation — manual regeneration required","Token metadata tracking is limited — only creation date, last used, expiration are tracked"],"requires":["Access to SchemaFlow dashboard","Database already connected to SchemaFlow","SchemaFlow account with appropriate permissions"],"input_types":["user action (generate/revoke token via dashboard)"],"output_types":["MCP token (string)","token metadata (JSON with creation date, expiration, status)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-schemaflow__cap_9","uri":"capability://automation.workflow.ai.ide.configuration.and.integration.setup","name":"ai-ide configuration and integration setup","description":"SchemaFlow provides IDE-specific configuration guides for Cursor IDE, Windsurf IDE, and VS Code + Cline, documenting how to register the SchemaFlow MCP server and authenticate with tokens. The configuration process involves adding SchemaFlow to the IDE's MCP server list with the server URL (api.schemaflow.dev/mcp/) and authentication token, then restarting the IDE to activate the connection. Each IDE has slightly different configuration syntax and file locations, but the underlying MCP registration mechanism is identical.","intents":["I want to set up SchemaFlow in my Cursor IDE so my AI assistant can access my database schema","I need to configure SchemaFlow for VS Code + Cline extension","I want to enable SchemaFlow in Windsurf IDE"],"best_for":["developers using Cursor, Windsurf, or VS Code + Cline","teams setting up SchemaFlow for the first time","IDE administrators configuring MCP servers for their team"],"limitations":["Configuration is IDE-specific — no universal setup process","Requires manual editing of IDE configuration files (no GUI setup wizard)","IDE restart required after configuration changes","No validation of configuration before IDE restart — errors only appear after restart","Only three IDEs are officially supported; other IDEs with MCP support are not documented"],"requires":["Cursor 0.30+, Windsurf, or VS Code with Cline extension","Valid MCP token from SchemaFlow dashboard","SchemaFlow server URL (api.schemaflow.dev/mcp/)"],"input_types":["IDE configuration file (JSON or YAML)","MCP token (string)"],"output_types":["configured IDE with active SchemaFlow MCP server"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["PostgreSQL 12+ or Supabase account","Node.js 16+ for running SchemaFlow server","HTTPS connection to api.schemaflow.dev/mcp/ (no HTTP fallback)","Valid MCP token generated from SchemaFlow dashboard","Initial database connection to populate cache","Access to SchemaFlow dashboard to trigger refresh","Network connectivity to api.schemaflow.dev for cache updates","Access to SchemaFlow dashboard","Database connectivity from SchemaFlow server","All connected IDEs must be online to receive updated schema"],"failure_modes":["Schema metadata is cached and may lag behind live database changes until manually refreshed via dashboard","Token revocation is not instantaneous — cached tokens remain valid until expiration","No fine-grained column-level access control — tokens grant access to entire cached schema","Schema changes are not automatically detected — manual refresh required via dashboard","Cache consistency depends on user discipline; stale schema can cause code generation errors","No incremental sync — full schema is re-cached on refresh, not delta updates","Cache size grows linearly with schema complexity; very large schemas (1000+ tables) may impact performance","Schema changes are not automatically detected — manual refresh required","Refresh is blocking — all connected IDEs receive updated schema simultaneously","No incremental sync — full schema is re-cached, not delta updates","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"ecosystem":0.39999999999999997,"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":"2026-06-17T09:51:04.049Z","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=schemaflow","compare_url":"https://unfragile.ai/compare?artifact=schemaflow"}},"signature":"IJDEVlQMuLXPkuLjFwdkKfA/H43wQPOuFIM+N5PZgG14i3ShyB8XcxvSIzw8te413jqkx7t2toFTJv55hBZ9Dg==","signedAt":"2026-06-20T13:53:55.473Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/schemaflow","artifact":"https://unfragile.ai/schemaflow","verify":"https://unfragile.ai/api/v1/verify?slug=schemaflow","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"}}