{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-database","slug":"database","name":"Database","type":"mcp","url":"https://github.com/TheRaLabs/legion-mcp","page_url":"https://unfragile.ai/database","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-database__cap_0","uri":"capability://tool.use.integration.multi.database.unified.query.execution.via.mcp.protocol","name":"multi-database unified query execution via mcp protocol","description":"Executes SQL queries against 8+ database systems (PostgreSQL, MySQL, SQL Server, BigQuery, Oracle, SQLite, Redshift, CockroachDB) through a single MCP tool interface. Routes queries through the Legion Query Runner abstraction layer, which handles database-specific connection management, SQL dialect normalization, result set formatting, and connection pooling. The FastMCP server maintains a DbContext state manager that tracks active database connections and query history across multiple database instances.","intents":["Execute SQL queries against multiple database types without managing separate clients","Switch between databases in a single AI agent workflow","Normalize query results across heterogeneous database systems","Maintain persistent connections with automatic pooling and error recovery"],"best_for":["AI agents requiring multi-database access in unified workflows","Teams building database-aware LLM applications","Developers migrating from REST-based database APIs to MCP protocol"],"limitations":["Query execution latency depends on network round-trip to database; no local query caching","Result set size limited by MCP message serialization constraints","No built-in query optimization or explain plan analysis","Connection pooling managed per database instance — no cross-database transaction support"],"requires":["Python 3.9+","Database credentials and connection strings for target systems","MCP-compatible client (Claude Desktop, custom MCP host, or Cline)","Network access to database endpoints"],"input_types":["SQL query strings","database identifier (from configured DB_TYPE or multi-database array)","optional query parameters"],"output_types":["structured JSON result sets","query execution metadata (rows affected, execution time)","error messages with database-specific error codes"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-database__cap_1","uri":"capability://memory.knowledge.automated.database.schema.discovery.and.mcp.resource.exposure","name":"automated database schema discovery and mcp resource exposure","description":"Automatically discovers database schemas (tables, columns, constraints, indexes) and exposes them as MCP Resources in a standardized JSON hierarchical format. The system introspects the connected database on initialization, generates schema metadata, and makes this information available to AI clients without requiring manual schema definition. Supports schema discovery across all 8 supported database types with database-specific introspection queries.","intents":["Provide AI agents with complete schema context without manual documentation","Enable schema-aware code generation for database queries","Expose table relationships and constraints for intelligent query construction","Maintain schema freshness through periodic or on-demand rediscovery"],"best_for":["AI agents generating SQL queries from natural language","Teams building database-aware code generation tools","Developers needing schema context in LLM prompts without manual updates"],"limitations":["Schema discovery latency scales with database size; large schemas (1000+ tables) may add 5-30 seconds to initialization","No incremental schema updates — full rediscovery required for schema changes","View definitions and computed columns may not be fully captured in all database systems","Constraint discovery depends on database system metadata completeness (Oracle and SQL Server have better constraint metadata than SQLite)"],"requires":["Active database connection with appropriate read permissions","Database user with SELECT privileges on information_schema or system catalogs","Python 3.9+"],"input_types":["database connection configuration","optional schema filter patterns"],"output_types":["JSON schema hierarchy with tables, columns, data types, constraints","MCP Resource objects exposing schema as text/plain or application/json"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-database__cap_10","uri":"capability://data.processing.analysis.postgresql.compatible.database.support.redshift.cockroachdb","name":"postgresql-compatible database support (redshift, cockroachdb)","description":"Provides native support for PostgreSQL-compatible databases (Redshift, CockroachDB) by leveraging PostgreSQL drivers and SQL dialect compatibility. These systems are treated as PostgreSQL variants in the Legion Query Runner, using the same connection management and query execution paths as native PostgreSQL while handling system-specific quirks (e.g., Redshift's distributed query optimization, CockroachDB's distributed transaction semantics).","intents":["Execute queries against Redshift data warehouses","Access CockroachDB distributed SQL databases","Leverage PostgreSQL driver compatibility for these systems","Maintain consistent query semantics across PostgreSQL variants"],"best_for":["Teams using Redshift for data warehouse analytics","Developers building applications on CockroachDB","Organizations with mixed PostgreSQL/Redshift/CockroachDB deployments"],"limitations":["Redshift-specific features (spectrum, materialized views) may not be fully supported","CockroachDB distributed transaction semantics differ from PostgreSQL; some queries may behave unexpectedly","Query optimization strategies differ between systems; same query may have different performance","Some PostgreSQL extensions not available in Redshift or CockroachDB"],"requires":["Python 3.9+","psycopg2 driver (compatible with both Redshift and CockroachDB)","Network access to Redshift or CockroachDB endpoints","Appropriate credentials and connection strings"],"input_types":["SQL queries compatible with PostgreSQL dialect","database connection configuration for Redshift or CockroachDB"],"output_types":["query results in PostgreSQL format","execution metadata"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-database__cap_11","uri":"capability://data.processing.analysis.cloud.database.integration.bigquery.oracle","name":"cloud database integration (bigquery, oracle)","description":"Provides native support for cloud and enterprise databases (BigQuery, Oracle) through specialized drivers and API integrations. BigQuery uses the google-cloud-bigquery SDK for cloud API integration, while Oracle uses cx_Oracle for enterprise database access. Each system has database-specific connection management, authentication handling, and result formatting through the Legion Query Runner abstraction.","intents":["Execute queries against Google BigQuery data warehouses","Access Oracle enterprise databases","Integrate cloud and enterprise databases into multi-database workflows","Handle cloud-specific authentication and API requirements"],"best_for":["Teams using BigQuery for cloud analytics","Organizations with Oracle enterprise databases","Developers building multi-cloud or hybrid database applications"],"limitations":["BigQuery queries incur per-query costs; high-frequency queries may be expensive","Oracle requires enterprise licensing and specialized driver installation","BigQuery authentication requires Google Cloud credentials and project setup","Oracle connection strings require specific TNS configuration or connection descriptor syntax","BigQuery result sets limited to 10GB per query; large datasets require pagination"],"requires":["Python 3.9+","google-cloud-bigquery SDK (for BigQuery)","cx_Oracle driver (for Oracle)","Google Cloud credentials and project ID (for BigQuery)","Oracle database credentials and connection descriptor (for Oracle)"],"input_types":["SQL queries compatible with BigQuery or Oracle dialect","database connection configuration"],"output_types":["query results in BigQuery or Oracle format","execution metadata including cost (BigQuery) or execution plan (Oracle)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-database__cap_2","uri":"capability://automation.workflow.flexible.multi.database.configuration.from.environment.cli.and.mcp.settings","name":"flexible multi-database configuration from environment, cli, and mcp settings","description":"Supports configuration of single or multiple databases through three independent configuration sources: environment variables (DB_TYPE/DB_CONFIG or DB_CONFIGS), command-line arguments (--db-type/--db-config or --db-configs), and MCP settings JSON. The system automatically processes configurations, generates unique database IDs, initializes Legion Query Runners for each database, and maintains runtime state including query history. Configuration precedence follows: MCP settings > CLI arguments > environment variables.","intents":["Configure database connections without code changes using environment variables","Support multi-database setups with dynamic configuration at runtime","Enable different database configurations per deployment environment","Manage database credentials securely through environment or MCP settings"],"best_for":["DevOps teams deploying database-mcp across multiple environments","Developers building multi-tenant AI applications with per-tenant databases","Teams using Docker/Kubernetes requiring environment-based configuration"],"limitations":["Configuration changes require server restart — no hot-reload of database connections","No built-in credential rotation or secret management integration","MCP settings JSON must be valid JSON; parsing errors fail silently in some cases","Multi-database configuration limited to array size constraints (typically 50+ databases supported, but context management overhead increases with count)"],"requires":["Python 3.9+","Environment variables or CLI arguments or MCP settings JSON with database credentials","Valid database connection strings for each configured database"],"input_types":["environment variables (DB_TYPE, DB_CONFIG, DB_CONFIGS)","CLI arguments (--db-type, --db-config, --db-configs)","MCP settings JSON object with database array"],"output_types":["DbConfig objects with generated IDs","initialized Legion Query Runner instances","runtime state including query history"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-database__cap_3","uri":"capability://automation.workflow.database.agnostic.connection.pooling.and.lifecycle.management","name":"database-agnostic connection pooling and lifecycle management","description":"Manages connection pooling, lifecycle, and error recovery for each database system through the Legion Query Runner abstraction. Handles database-specific connection management (native drivers for PostgreSQL/MySQL/SQL Server, cloud API integration for BigQuery, file-based connections for SQLite) with automatic connection validation, timeout handling, and graceful degradation. The DbContext state manager tracks active connections and maintains query history across the server lifetime.","intents":["Maintain efficient database connections without manual pool configuration","Handle connection failures and timeouts transparently","Support long-running AI agent workflows with persistent database access","Manage resource cleanup on server shutdown"],"best_for":["Long-running AI agents requiring persistent database access","High-concurrency environments with multiple simultaneous queries","Teams needing transparent connection failure handling"],"limitations":["Connection pool size not configurable per database — uses driver defaults (typically 5-20 connections)","No cross-database transaction support — each database maintains independent connection pools","Connection timeout behavior varies by database driver; some systems may hang on network failures","Query history stored in memory — no persistence across server restarts"],"requires":["Python 3.9+","Database-specific drivers: psycopg2 (PostgreSQL), mysql-connector-python (MySQL), pyodbc (SQL Server), google-cloud-bigquery (BigQuery), cx_Oracle (Oracle), sqlite3 (SQLite)","Network connectivity to database endpoints"],"input_types":["database connection configuration","query execution requests"],"output_types":["connection status metadata","query execution results","error messages with connection diagnostics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-database__cap_4","uri":"capability://tool.use.integration.mcp.tool.based.database.operation.interface","name":"mcp tool-based database operation interface","description":"Exposes database operations as MCP Tools with standardized input schemas and output formats. Each tool accepts database identifiers, SQL queries, and optional parameters, returning structured results with execution metadata. The FastMCP server registers tools dynamically based on configured databases, enabling AI clients to discover and invoke database operations through the MCP protocol's tool-calling mechanism.","intents":["Enable AI agents to discover available database operations through MCP tool discovery","Provide type-safe database operation invocation with schema validation","Support natural language to SQL translation with validated tool inputs","Integrate database access into multi-step AI agent workflows"],"best_for":["AI agents using tool-calling for database access","Teams building database-aware LLM applications with Claude or other MCP-compatible clients","Developers needing standardized database operation interfaces"],"limitations":["Tool schemas must be JSON-serializable; complex query parameters require string encoding","No built-in query validation or SQL injection prevention — relies on parameterized queries","Tool discovery overhead increases with number of configured databases","MCP message size limits may constrain result set sizes (typically 100KB-1MB per message)"],"requires":["MCP-compatible client with tool-calling support","Python 3.9+","Configured database connections"],"input_types":["tool input JSON with database identifier, SQL query, optional parameters"],"output_types":["structured JSON results with rows, column metadata, execution time","error responses with database error codes and messages"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-database__cap_5","uri":"capability://data.processing.analysis.sql.dialect.normalization.and.query.translation","name":"sql dialect normalization and query translation","description":"Normalizes SQL queries across different database systems by handling dialect-specific syntax differences. The Legion Query Runner translates queries for database-specific requirements (e.g., BigQuery's LIMIT vs SQL Server's TOP, PostgreSQL's RETURNING vs MySQL's LAST_INSERT_ID), manages result set formatting, and handles error translation. Supports parameterized queries to prevent SQL injection while maintaining dialect compatibility.","intents":["Execute the same query logic across different database systems","Translate natural language queries to database-specific SQL","Handle database-specific result formatting transparently","Prevent SQL injection through parameterized query support"],"best_for":["Multi-database AI agents requiring consistent query semantics","Teams building database-agnostic query generation tools","Developers needing SQL dialect abstraction"],"limitations":["Complex dialect-specific features (window functions, CTEs) may not translate perfectly across all systems","Query optimization is database-specific — same query may have different performance across systems","Some dialect features (e.g., PostgreSQL JSON operators) have no direct equivalents in other systems","Parameterized query support varies by database driver; some systems have limited parameter binding"],"requires":["Python 3.9+","Database-specific drivers with parameterized query support","SQL queries compatible with target database dialect"],"input_types":["SQL query strings","optional query parameters for parameterized queries"],"output_types":["database-specific SQL with normalized syntax","formatted result sets with column metadata","error messages with dialect-specific error codes"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-database__cap_6","uri":"capability://memory.knowledge.query.history.tracking.and.execution.metadata.capture","name":"query history tracking and execution metadata capture","description":"Maintains a runtime query history in the DbContext state manager, capturing execution metadata for each query including execution time, rows affected, error status, and timestamps. This enables AI agents to reference previous queries, understand query performance, and debug execution issues. Query history is scoped per database instance and persists for the server lifetime.","intents":["Enable AI agents to reference and learn from previous queries","Provide execution performance metrics for query optimization","Support debugging and auditing of database operations","Track query patterns for analysis and optimization"],"best_for":["AI agents requiring query context and history","Teams building database query optimization tools","Developers needing query execution auditing"],"limitations":["Query history stored in memory only — lost on server restart","No built-in persistence to external storage (database, file system)","History size unbounded — may consume significant memory in long-running servers","No query result caching — same query executed multiple times incurs full execution cost"],"requires":["Python 3.9+","Active database connections"],"input_types":["query execution requests"],"output_types":["query history entries with execution metadata","performance metrics (execution time, rows affected)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-database__cap_7","uri":"capability://planning.reasoning.mcp.prompt.templates.for.database.interaction.guidance","name":"mcp prompt templates for database interaction guidance","description":"Exposes MCP Prompts that provide pre-written guidance and examples for database interaction patterns. These prompts guide AI clients on how to construct queries, handle errors, and optimize database access through the MCP protocol. Prompts are statically defined and serve as contextual hints for AI agents using the database-mcp server.","intents":["Provide AI agents with best practices for database query construction","Guide error handling and retry logic for database operations","Offer examples of multi-database query patterns","Establish conventions for database access in AI workflows"],"best_for":["AI agents requiring guidance on database interaction patterns","Teams building database-aware LLM applications","Developers needing standardized database access conventions"],"limitations":["Prompts are static — no dynamic generation based on schema or query history","Limited to text-based guidance; no interactive examples or tutorials","Prompt effectiveness depends on AI client's ability to follow instructions","No feedback mechanism to improve prompts based on query success/failure"],"requires":["MCP-compatible client with prompt support","Python 3.9+"],"input_types":["prompt name or identifier"],"output_types":["text-based guidance and examples","best practices for database access"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-database__cap_8","uri":"capability://safety.moderation.database.specific.error.handling.and.translation","name":"database-specific error handling and translation","description":"Translates database-specific error codes and messages into standardized error responses through the Legion Query Runner abstraction. Handles connection errors, syntax errors, constraint violations, and timeout errors with appropriate error messages and recovery suggestions. Error handling is database-specific, leveraging native driver error information.","intents":["Provide meaningful error messages for database operation failures","Enable AI agents to handle database errors intelligently","Support error recovery and retry logic","Debug database connectivity and query issues"],"best_for":["AI agents requiring robust error handling","Teams building resilient database-driven applications","Developers needing database error diagnostics"],"limitations":["Error message quality varies by database system; some systems provide minimal error context","No automatic error recovery — AI agents must implement retry logic","Connection errors may not be immediately distinguishable from query errors","Some database systems (BigQuery, Oracle) have complex error codes requiring specialized knowledge"],"requires":["Python 3.9+","Database-specific drivers with error information"],"input_types":["query execution requests"],"output_types":["standardized error responses with database error codes","error messages with recovery suggestions"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-database__cap_9","uri":"capability://automation.workflow.fastmcp.server.lifecycle.and.context.management","name":"fastmcp server lifecycle and context management","description":"Implements a FastMCP server with lifespan context management that handles initialization, configuration loading, database connection setup, and graceful shutdown. The DbContext state manager maintains global state including active database connections, query history, and configuration metadata. Server lifecycle hooks manage resource allocation and cleanup across all configured databases.","intents":["Initialize database connections on server startup","Manage server lifecycle with proper resource cleanup","Maintain global state across multiple database instances","Support graceful shutdown with connection cleanup"],"best_for":["Teams deploying database-mcp as a long-running service","Developers building MCP servers with complex state management","DevOps teams requiring reliable server lifecycle handling"],"limitations":["No built-in health checks or liveness probes for monitoring","Server state not persisted — query history and connections lost on restart","No graceful degradation if some database connections fail during startup","Shutdown timeout not configurable — may hang if databases are unresponsive"],"requires":["Python 3.9+","FastMCP framework","Database connection configurations"],"input_types":["server configuration","database configurations"],"output_types":["initialized FastMCP server instance","DbContext state manager"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Database credentials and connection strings for target systems","MCP-compatible client (Claude Desktop, custom MCP host, or Cline)","Network access to database endpoints","Active database connection with appropriate read permissions","Database user with SELECT privileges on information_schema or system catalogs","psycopg2 driver (compatible with both Redshift and CockroachDB)","Network access to Redshift or CockroachDB endpoints","Appropriate credentials and connection strings","google-cloud-bigquery SDK (for BigQuery)"],"failure_modes":["Query execution latency depends on network round-trip to database; no local query caching","Result set size limited by MCP message serialization constraints","No built-in query optimization or explain plan analysis","Connection pooling managed per database instance — no cross-database transaction support","Schema discovery latency scales with database size; large schemas (1000+ tables) may add 5-30 seconds to initialization","No incremental schema updates — full rediscovery required for schema changes","View definitions and computed columns may not be fully captured in all database systems","Constraint discovery depends on database system metadata completeness (Oracle and SQL Server have better constraint metadata than SQLite)","Redshift-specific features (spectrum, materialized views) may not be fully supported","CockroachDB distributed transaction semantics differ from PostgreSQL; some queries may behave unexpectedly","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":"2026-06-17T09:51:03.037Z","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=database","compare_url":"https://unfragile.ai/compare?artifact=database"}},"signature":"UuboXrIexm0HQ91+wQHPIezSisJ3UNsnzxJkmWEeuSm0yAyORpMG5rRlfindcJP6PS45S0R3xfw397vuZAPWDQ==","signedAt":"2026-06-20T17:18:19.826Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/database","artifact":"https://unfragile.ai/database","verify":"https://unfragile.ai/api/v1/verify?slug=database","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"}}