{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-dbeaver--dbeaver","slug":"dbeaver--dbeaver","name":"dbeaver","type":"product","url":"https://dbeaver.io","page_url":"https://unfragile.ai/dbeaver--dbeaver","categories":["data-analysis"],"tags":["ai","copilot","database","db2","dbeaver","erd","gui","java","jdbc","mysql","nosql","openai","oracle","postgresql","redshift","sql","sqlite","sqlserver"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-dbeaver--dbeaver__cap_0","uri":"capability://tool.use.integration.multi.database.connection.management.with.unified.jdbc.abstraction","name":"multi-database connection management with unified jdbc abstraction","description":"DBeaver abstracts heterogeneous database connections through a plugin-based driver management system built on JDBC, where each database type (PostgreSQL, Oracle, MySQL, SQL Server, DB2, etc.) is implemented as a specialized extension plugin that registers custom DataSourceProvider implementations. The core Data Source Management layer maintains connection pooling, credential encryption, and lifecycle management through a centralized registry that maps logical data sources to physical JDBC drivers, enabling seamless switching between 50+ database systems without code changes.","intents":["Connect to multiple databases simultaneously from a single interface","Manage credentials securely across different database systems","Switch between database connections without restarting the application","Extend support for custom or proprietary databases via plugin architecture"],"best_for":["Database administrators managing heterogeneous infrastructure","Developers working with multiple database backends in microservices","Teams needing a unified database client across Oracle, PostgreSQL, MySQL, SQL Server"],"limitations":["JDBC driver availability required for each database type — proprietary databases may lack drivers","Connection pooling overhead adds ~50-100ms per new connection initialization","No built-in connection failover or automatic replica detection — requires manual configuration","Encrypted credential storage tied to local machine keystore — credentials not portable across machines"],"requires":["Java 11+ runtime environment","JDBC driver JAR files for target databases (auto-downloaded for common databases)","Network access to database servers with appropriate firewall rules"],"input_types":["connection parameters (host, port, database name, username, password)","JDBC connection strings","SSH tunnel configuration for remote databases"],"output_types":["active database connection object","connection metadata (version, capabilities, supported features)","connection status and diagnostics"],"categories":["tool-use-integration","database-connectivity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dbeaver--dbeaver__cap_1","uri":"capability://code.generation.editing.sql.dialect.aware.query.editing.with.syntax.completion.and.validation","name":"sql dialect-aware query editing with syntax completion and validation","description":"DBeaver implements a SQL Editor System with a pluggable SQL Dialect System that parses and validates SQL syntax specific to each database engine (PostgreSQL, Oracle, T-SQL, MySQL dialects). The editor uses a custom syntax tokenizer and AST-like parsing to provide real-time syntax highlighting, context-aware code completion, and query validation without executing the query. Each database extension registers its own SQLDialect implementation that defines reserved keywords, functions, operators, and syntax rules, enabling the editor to catch errors before execution and suggest database-specific functions.","intents":["Write SQL queries with real-time syntax validation for the target database","Get autocomplete suggestions for tables, columns, functions specific to the connected database","Identify SQL syntax errors before execution to avoid wasted database calls","Format and refactor SQL code according to database-specific conventions"],"best_for":["SQL developers working with multiple database dialects (Oracle PL/SQL vs PostgreSQL vs T-SQL)","Teams enforcing SQL code quality standards before deployment","DBAs writing complex queries and stored procedures with confidence"],"limitations":["Syntax validation is best-effort and may not catch all database-specific semantic errors","Autocomplete requires active database connection to fetch schema metadata — offline editing has limited suggestions","Custom or user-defined functions not automatically discovered — requires manual registration","Performance degrades with very large result sets (>100K rows) in the editor's syntax tree"],"requires":["Active connection to target database for schema-aware completion","Database metadata permissions (SELECT on information_schema or equivalent)","Java 11+ for the editor runtime"],"input_types":["SQL query text (partial or complete)","cursor position for context-aware completion","database connection context"],"output_types":["syntax-highlighted SQL text","completion suggestions (tables, columns, functions, keywords)","validation errors with line/column positions","formatted SQL code"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dbeaver--dbeaver__cap_10","uri":"capability://image.visual.entity.relationship.diagram.erd.visualization.and.generation","name":"entity-relationship diagram (erd) visualization and generation","description":"DBeaver can generate Entity-Relationship Diagrams (ERDs) from database schema, visualizing tables, columns, and foreign key relationships as a diagram. The ERD engine queries database metadata to extract table structures and relationships, then renders them as a visual graph with customizable layout options. Users can export ERDs as images (PNG, SVG) or as documentation. The diagram is interactive, allowing users to navigate to table definitions or edit tables directly from the diagram.","intents":["Visualize database schema structure and relationships","Generate documentation of database design","Identify missing or incorrect foreign key relationships","Understand complex schemas with many tables and relationships"],"best_for":["DBAs documenting database design","Teams onboarding new developers to understand schema","Architects reviewing database design for correctness"],"limitations":["ERD generation is schema-only — does not show data volumes or usage patterns","Large schemas (>100 tables) produce cluttered diagrams that are hard to read","Layout algorithms may produce suboptimal arrangements — manual adjustment often needed","No support for logical vs. physical schema separation — all tables shown equally","Relationship visualization limited to foreign keys — does not show application-level relationships"],"requires":["Active database connection with metadata query permissions","Sufficient memory for rendering large diagrams (>1000 tables may cause performance issues)"],"input_types":["database connection","schema/table filter criteria","layout algorithm selection"],"output_types":["ERD visualization (interactive diagram in UI)","exported ERD image (PNG, SVG, PDF)","ERD documentation (HTML, Markdown)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dbeaver--dbeaver__cap_11","uri":"capability://code.generation.editing.stored.procedure.and.function.debugging.with.execution.tracing","name":"stored procedure and function debugging with execution tracing","description":"DBeaver provides debugging capabilities for stored procedures and functions in databases that support it (PostgreSQL, Oracle, SQL Server). Users can set breakpoints in procedure code, step through execution, inspect variable values, and view the call stack. The debugger integrates with the SQL editor and uses database-specific debugging APIs (e.g., PL/pgSQL debugger for PostgreSQL) to control execution. Execution traces show which lines were executed and how many times, useful for performance analysis.","intents":["Debug stored procedures and functions without external debugging tools","Identify logic errors in complex PL/SQL or PL/pgSQL code","Trace execution flow and variable values during procedure execution","Optimize procedures by analyzing execution traces"],"best_for":["Database developers writing complex stored procedures","DBAs troubleshooting misbehaving procedures in production","Teams maintaining legacy PL/SQL or PL/pgSQL codebases"],"limitations":["Debugging support varies by database — not all databases support procedure debugging","Requires special database extensions or permissions (e.g., PL/pgSQL debugger extension for PostgreSQL)","Debugging may impact database performance — not suitable for production debugging without caution","Limited to single-threaded debugging — cannot debug concurrent procedure executions","Variable inspection limited to scalar types — complex types may not display correctly"],"requires":["Active database connection with debugging permissions","Database-specific debugging extension or API support (varies by database)","Stored procedure source code accessible in the database"],"input_types":["stored procedure or function name","input parameters for procedure execution","breakpoint locations (line numbers)"],"output_types":["execution trace with line-by-line execution","variable values at each step","call stack and execution context","performance metrics (execution time per line)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dbeaver--dbeaver__cap_12","uri":"capability://automation.workflow.database.backup.and.restore.with.scheduling","name":"database backup and restore with scheduling","description":"DBeaver provides backup and restore functionality for databases, allowing users to create full or partial backups and restore them later. The backup engine uses database-native tools (mysqldump for MySQL, pg_dump for PostgreSQL, RMAN for Oracle) to create backups, and supports scheduling backups to run automatically on a schedule. Backups can be compressed and encrypted for security. The restore functionality allows selective restoration of specific tables or schemas.","intents":["Create backups of databases for disaster recovery","Schedule automated backups to run on a regular schedule","Restore databases from backups after data loss or corruption","Migrate data between databases using backup/restore"],"best_for":["DBAs managing backup and recovery procedures","Teams requiring automated backup schedules","Organizations with disaster recovery requirements"],"limitations":["Backup performance depends on database size and network speed — large databases may take hours","Backups stored locally on the DBeaver machine — requires external storage for off-site backups","No incremental backup support — each backup is a full backup, consuming significant disk space","Restore operations may lock the database — not suitable for production without downtime","Backup encryption keys stored locally — loss of DBeaver machine may prevent backup recovery"],"requires":["Active database connection with backup permissions","Database-native backup tools installed (mysqldump, pg_dump, etc.)","Sufficient disk space for backups","Appropriate file system permissions for backup directory"],"input_types":["database connection","backup scope (full database, specific schemas/tables)","backup schedule (one-time or recurring)","compression and encryption options"],"output_types":["backup file (compressed, encrypted if requested)","backup metadata (timestamp, size, database version)","restore status and logs"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dbeaver--dbeaver__cap_2","uri":"capability://data.processing.analysis.query.execution.with.result.set.streaming.and.in.memory.caching","name":"query execution with result set streaming and in-memory caching","description":"DBeaver's Query Execution engine submits SQL queries to the database via JDBC and streams results into a configurable in-memory cache that supports pagination and lazy-loading of rows. The Result Set Viewer component renders results in a tabular format with support for filtering, sorting, and exporting. The execution layer manages statement lifecycle, timeout handling, and transaction context, with options to execute in auto-commit mode or within explicit transactions. Large result sets are streamed rather than fully loaded to prevent memory exhaustion.","intents":["Execute SQL queries and view results in a formatted table","Handle large result sets (millions of rows) without loading entire dataset into memory","Filter and sort results without re-executing the query","Export query results to CSV, JSON, or other formats"],"best_for":["Analysts exploring large datasets interactively","DBAs running diagnostic queries on production databases","Developers testing SQL queries during development"],"limitations":["Sorting and filtering on large result sets requires re-executing the query or loading full dataset — no client-side indexing","Result set caching is in-memory only — no persistence to disk for later analysis","Timeout handling is database-dependent — some databases may not respect JDBC timeout settings","Binary data (BLOB, CLOB) requires special handling and may not display correctly in the table view"],"requires":["Active database connection with appropriate permissions","Sufficient heap memory for result set caching (configurable, default 100MB)","JDBC driver supporting streaming result sets"],"input_types":["SQL query text","execution parameters (timeout, fetch size, transaction mode)","filter and sort criteria"],"output_types":["tabular result set with metadata (column names, types)","execution statistics (rows affected, execution time)","exported data (CSV, JSON, XML, SQL INSERT statements)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dbeaver--dbeaver__cap_3","uri":"capability://memory.knowledge.database.schema.navigation.and.metadata.introspection","name":"database schema navigation and metadata introspection","description":"DBeaver's Navigator System provides a hierarchical tree view of database schema objects (tables, views, stored procedures, functions, indexes, constraints) by querying database metadata through JDBC DatabaseMetaData API and database-specific system catalogs. Each database extension implements a custom MetaModel that defines how to query and cache schema metadata efficiently. The navigator supports lazy-loading of schema objects to avoid expensive metadata queries upfront, with background refresh capabilities to detect schema changes. Metadata is cached locally with configurable TTL to balance freshness vs. performance.","intents":["Browse database schema structure without writing metadata queries","Quickly locate tables, columns, and relationships in large schemas","View table definitions, constraints, and indexes visually","Detect schema changes and refresh metadata when databases are modified externally"],"best_for":["DBAs exploring unfamiliar database schemas","Developers building queries against databases they don't fully understand","Teams documenting database structure for compliance or knowledge transfer"],"limitations":["Metadata caching can become stale if schema is modified outside DBeaver — manual refresh required","Large schemas (>10K tables) may cause UI lag when expanding nodes due to metadata query overhead","Some database systems (NoSQL, data warehouses) have limited metadata support — navigator may be incomplete","Permissions-based schema filtering depends on database user role — some objects may be hidden if user lacks permissions"],"requires":["Active database connection with metadata query permissions","JDBC driver supporting DatabaseMetaData API","Sufficient memory for schema metadata caching (typically <100MB for most schemas)"],"input_types":["database connection context","schema/catalog filter criteria","refresh trigger (manual or automatic)"],"output_types":["hierarchical schema tree (databases → schemas → tables → columns)","object metadata (data types, constraints, indexes, comments)","DDL statements for selected objects"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dbeaver--dbeaver__cap_4","uri":"capability://data.processing.analysis.data.editing.and.persistence.with.transaction.management","name":"data editing and persistence with transaction management","description":"DBeaver's Data Editing and Persistence layer allows in-place editing of table data in the result set viewer, with automatic change tracking and transaction management. When a user modifies a cell, DBeaver generates the appropriate UPDATE, INSERT, or DELETE statement based on the table's primary key and constraints, executes it within a transaction, and rolls back on error. The system supports batch operations for editing multiple rows, with options for auto-commit or manual transaction control. Changes are tracked in memory until explicitly committed, allowing users to review and undo changes before persisting.","intents":["Edit table data directly in the result set viewer without writing SQL","Bulk update or delete multiple rows with transaction safety","Undo changes before committing to the database","Generate and review SQL statements before executing data modifications"],"best_for":["DBAs performing ad-hoc data corrections or migrations","Developers testing data scenarios without writing UPDATE/DELETE statements","Teams requiring audit trails of data modifications (with transaction logging)"],"limitations":["Editing requires table to have a primary key or unique constraint — tables without keys cannot be edited","Complex data types (JSON, arrays, custom types) may not be editable in the UI — requires manual SQL","Batch operations on very large datasets (>100K rows) may cause performance issues or memory exhaustion","No built-in audit logging — modifications are not tracked unless database has triggers or audit tables","Concurrent modifications by other users are not detected — potential for lost updates without explicit locking"],"requires":["Active database connection with INSERT/UPDATE/DELETE permissions","Table must have a primary key or unique constraint for UPDATE/DELETE operations","Transaction support in the target database"],"input_types":["cell value edits in result set viewer","batch edit operations (multi-row updates)","transaction mode (auto-commit vs. manual)"],"output_types":["generated SQL statements (UPDATE, INSERT, DELETE)","transaction status (pending, committed, rolled back)","affected row counts"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dbeaver--dbeaver__cap_5","uri":"capability://tool.use.integration.database.specific.extension.framework.with.plugin.architecture","name":"database-specific extension framework with plugin architecture","description":"DBeaver's Plugin System is built on Eclipse RCP and allows third-party developers to extend support for new databases or add custom functionality through well-defined extension points. Each database extension (org.jkiss.dbeaver.ext.*) implements core interfaces like DataSourceProvider, SQLDialect, and MetaModel to register custom connection handling, SQL syntax rules, and metadata queries. The plugin system uses OSGi bundles for dependency management and lazy-loading, ensuring that unused database drivers don't consume memory. Custom plugins can hook into the UI, query execution, and data presentation layers.","intents":["Add support for custom or proprietary databases not included in DBeaver","Extend DBeaver with domain-specific features (data masking, compliance checks, custom exporters)","Integrate DBeaver with external tools via plugin APIs","Customize UI and behavior for specific organizational requirements"],"best_for":["Database vendors building native DBeaver support for proprietary systems","Organizations with custom databases requiring DBeaver integration","Tool developers building extensions on top of DBeaver's plugin ecosystem"],"limitations":["Plugin development requires Java and Eclipse RCP knowledge — steep learning curve for non-Java developers","Plugin API stability not guaranteed across major DBeaver versions — plugins may break on upgrades","No sandboxing — malicious plugins can access full system resources and database credentials","Limited documentation for plugin development — requires reading DBeaver source code to understand patterns","Plugin distribution and versioning not centralized — no official plugin marketplace"],"requires":["Java 11+ development environment","Eclipse IDE or Maven for building plugins","Understanding of OSGi bundle structure and Eclipse extension points","DBeaver source code or plugin SDK for development"],"input_types":["plugin manifest (MANIFEST.MF with extension point declarations)","Java classes implementing DataSourceProvider, SQLDialect, MetaModel interfaces","UI contributions (menus, dialogs, editors)"],"output_types":["compiled OSGi bundle (JAR file)","registered extension points in DBeaver UI","custom database support and features"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dbeaver--dbeaver__cap_6","uri":"capability://code.generation.editing.ai.powered.sql.query.generation.and.optimization.copilot.integration","name":"ai-powered sql query generation and optimization (copilot integration)","description":"DBeaver integrates with OpenAI's GPT models to provide AI-assisted SQL query generation and optimization. Users can describe their intent in natural language, and the AI generates corresponding SQL queries for the connected database. The integration sends the current database schema context (table names, column definitions) to the LLM along with the user's natural language request, and returns generated SQL that can be previewed and executed. The system also supports query optimization suggestions based on execution plans and schema analysis.","intents":["Generate SQL queries from natural language descriptions without writing SQL manually","Get optimization suggestions for slow-running queries","Learn SQL by seeing AI-generated examples for common patterns","Reduce time spent writing boilerplate SQL for routine operations"],"best_for":["Non-SQL developers who need to query databases","SQL developers looking to accelerate query writing","Teams adopting AI-assisted development workflows"],"limitations":["Requires OpenAI API key and active internet connection — not available offline","Generated queries may be syntactically correct but semantically wrong — requires manual review","AI context window limits the amount of schema information sent — very large schemas may not be fully represented","Cost per query generation (OpenAI API charges) — not suitable for high-volume automated query generation","Privacy concerns — schema and query context sent to external LLM service, may violate data governance policies","AI model knowledge cutoff may not include latest database features or syntax"],"requires":["OpenAI API key (paid account)","Active internet connection to OpenAI API","Database connection with schema metadata accessible","DBeaver version with Copilot integration enabled"],"input_types":["natural language query description","database schema context (tables, columns, types)","execution plan for optimization requests"],"output_types":["generated SQL query text","optimization suggestions with rationale","alternative query approaches"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dbeaver--dbeaver__cap_7","uri":"capability://data.processing.analysis.data.transformation.and.export.with.multiple.format.support","name":"data transformation and export with multiple format support","description":"DBeaver's Data Transformation layer supports exporting query results and table data to multiple formats (CSV, JSON, XML, SQL INSERT statements, Excel, HTML) with customizable formatting options. The export engine streams data from the result set to avoid memory exhaustion on large datasets, and supports filtering and column selection before export. Users can configure delimiters, encoding, headers, and other format-specific options. The system also supports data import from CSV and other formats into tables.","intents":["Export query results to CSV or JSON for analysis in external tools","Generate SQL INSERT statements for data migration or backup","Import data from CSV files into database tables","Share query results with non-technical stakeholders in Excel or HTML format"],"best_for":["Data analysts exporting results for further analysis","DBAs performing data migrations or backups","Teams sharing database reports with stakeholders","Developers generating test data or seed scripts"],"limitations":["Export performance degrades with very large datasets (>10M rows) — streaming helps but still slow","Complex data types (JSON, arrays, custom types) may not export correctly to all formats","CSV export loses type information — all data exported as strings, requiring post-processing","No built-in data transformation (pivot, aggregate, join) — requires SQL for complex transformations","Import from CSV requires manual schema mapping — no automatic type inference"],"requires":["Active database connection for data source","Write permissions to target table for import operations","Sufficient disk space for exported files"],"input_types":["result set or table data","export format selection (CSV, JSON, XML, SQL, Excel, HTML)","format-specific options (delimiter, encoding, headers, column selection)"],"output_types":["exported file in selected format","import status and row counts for CSV import","data validation errors during import"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dbeaver--dbeaver__cap_8","uri":"capability://tool.use.integration.transaction.management.and.rollback.with.savepoint.support","name":"transaction management and rollback with savepoint support","description":"DBeaver provides explicit transaction control through a Transaction Management UI that allows users to start transactions, set savepoints, commit, or rollback changes. The system tracks transaction state and displays it in the UI, with options to auto-commit or manually control transactions. Savepoint support (where available in the database) allows rolling back to intermediate points within a transaction. The transaction layer integrates with data editing to ensure all modifications are atomic and can be rolled back together.","intents":["Execute multiple SQL statements as a single atomic transaction","Rollback changes if an error occurs during multi-step operations","Set savepoints to partially rollback within a transaction","Test data modifications safely before committing to production"],"best_for":["DBAs performing critical data modifications requiring atomicity","Developers testing complex multi-statement operations","Teams requiring audit trails of committed transactions"],"limitations":["Savepoint support varies by database — not all databases support nested transactions","Long-running transactions may lock resources and impact other users — no automatic timeout","Transaction isolation level depends on database configuration — dirty reads or phantom reads possible with low isolation","No distributed transaction support — cannot coordinate transactions across multiple databases","Implicit commits (DDL statements) may commit pending changes unexpectedly in some databases"],"requires":["Active database connection with transaction support","Appropriate permissions for transaction control (COMMIT, ROLLBACK)","Understanding of database isolation levels and locking behavior"],"input_types":["SQL statements to execute within transaction","transaction control commands (BEGIN, COMMIT, ROLLBACK, SAVEPOINT)","isolation level selection"],"output_types":["transaction status (active, committed, rolled back)","affected row counts per statement","transaction log with timestamps"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dbeaver--dbeaver__cap_9","uri":"capability://data.processing.analysis.database.comparison.and.schema.synchronization","name":"database comparison and schema synchronization","description":"DBeaver includes tools for comparing schemas between two databases and generating synchronization scripts to make them identical. The comparison engine analyzes table structures, indexes, constraints, and stored procedures in both databases, identifies differences, and generates ALTER TABLE or CREATE statements to synchronize them. This is useful for maintaining consistency between development, staging, and production databases. The system supports dry-run mode to preview changes before applying them.","intents":["Identify schema differences between development and production databases","Generate migration scripts to synchronize schemas across environments","Detect unintended schema drift in production","Validate that schema changes were applied correctly"],"best_for":["DBAs managing schema consistency across multiple environments","DevOps teams automating database deployment pipelines","Teams performing database migrations with schema validation"],"limitations":["Comparison is schema-only — does not detect data differences or data type mismatches","Generated synchronization scripts may not be optimal — manual review recommended for production","Complex schema objects (triggers, views with dependencies) may not synchronize correctly","No support for comparing data — only schema structure","Requires both databases to be accessible simultaneously — not suitable for offline comparison"],"requires":["Active connections to both source and target databases","Metadata query permissions on both databases","ALTER TABLE permissions on target database for applying changes"],"input_types":["source database connection","target database connection","schema/table filter criteria"],"output_types":["difference report (added, modified, deleted objects)","synchronization script (ALTER TABLE, CREATE statements)","dry-run preview of changes"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["Java 11+ runtime environment","JDBC driver JAR files for target databases (auto-downloaded for common databases)","Network access to database servers with appropriate firewall rules","Active connection to target database for schema-aware completion","Database metadata permissions (SELECT on information_schema or equivalent)","Java 11+ for the editor runtime","Active database connection with metadata query permissions","Sufficient memory for rendering large diagrams (>1000 tables may cause performance issues)","Active database connection with debugging permissions","Database-specific debugging extension or API support (varies by database)"],"failure_modes":["JDBC driver availability required for each database type — proprietary databases may lack drivers","Connection pooling overhead adds ~50-100ms per new connection initialization","No built-in connection failover or automatic replica detection — requires manual configuration","Encrypted credential storage tied to local machine keystore — credentials not portable across machines","Syntax validation is best-effort and may not catch all database-specific semantic errors","Autocomplete requires active database connection to fetch schema metadata — offline editing has limited suggestions","Custom or user-defined functions not automatically discovered — requires manual registration","Performance degrades with very large result sets (>100K rows) in the editor's syntax tree","ERD generation is schema-only — does not show data volumes or usage patterns","Large schemas (>100 tables) produce cluttered diagrams that are hard to read","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.42984493194314083,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:58:39.623Z","last_commit":"2026-05-03T11:51:16Z"},"community":{"stars":49837,"forks":4153,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=dbeaver--dbeaver","compare_url":"https://unfragile.ai/compare?artifact=dbeaver--dbeaver"}},"signature":"LEC5ZTy8kAHx65DV8wsyRbp+CIV22n/Mmw1bPsEBIxEzunH23H69L7Htvo7HiFVQEXh4Tsmnh0h7O0QCyYM6DA==","signedAt":"2026-06-20T08:05:22.488Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dbeaver--dbeaver","artifact":"https://unfragile.ai/dbeaver--dbeaver","verify":"https://unfragile.ai/api/v1/verify?slug=dbeaver--dbeaver","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"}}