{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-dreamfactory","slug":"dreamfactory","name":"DreamFactory","type":"mcp","url":"https://github.com/dreamfactorysoftware/df-mcp/","page_url":"https://unfragile.ai/dreamfactory","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-dreamfactory__cap_0","uri":"capability://tool.use.integration.rbac.gated.sql.query.execution.across.multi.database.backends","name":"rbac-gated sql query execution across multi-database backends","description":"Executes SQL queries against MS SQL Server, MySQL, PostgreSQL, and other data sources through an MCP server interface with role-based access control (RBAC) enforcement at the query level. The architecture intercepts database connections, applies user-scoped permission policies before query execution, and returns results only for authorized tables/columns, preventing unauthorized data access at the database abstraction layer rather than application layer.","intents":["I need to let Claude or other LLM agents query my production database without giving them full database credentials","I want to enforce column-level and table-level access policies when exposing database queries through an AI interface","I need to audit which data an AI agent accessed and when, with full RBAC traceability"],"best_for":["Enterprise teams exposing internal databases to AI agents with strict compliance requirements","Organizations migrating from REST APIs to MCP for database access with existing RBAC policies","Teams building multi-tenant AI applications requiring per-user data isolation"],"limitations":["RBAC enforcement depends on correct policy configuration — misconfigured policies bypass intended restrictions","Query complexity and performance depend on underlying database optimizer; MCP layer adds ~50-100ms overhead per query","No built-in query result caching — repeated queries hit the database each time unless implemented at application layer","Limited to databases with JDBC/ODBC drivers or native connection libraries; custom data sources require custom adapters"],"requires":["DreamFactory MCP server deployed and running","Network connectivity to target databases (on-premise or cloud)","Database credentials with appropriate permissions configured in DreamFactory","MCP client (Claude, Anthropic SDK, or compatible tool) with MCP server configuration","RBAC policies defined in DreamFactory for user roles and database access"],"input_types":["SQL query strings","database connection parameters","user identity/role context"],"output_types":["structured query results (JSON, CSV)","query execution metadata (rows affected, execution time)","access denial responses with policy violation details"],"categories":["tool-use-integration","data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dreamfactory__cap_1","uri":"capability://tool.use.integration.multi.database.connection.pooling.and.credential.management","name":"multi-database connection pooling and credential management","description":"Manages persistent connection pools to multiple heterogeneous databases (MS SQL Server, MySQL, PostgreSQL, etc.) with centralized credential storage and rotation support. The MCP server maintains a registry of database connections, handles connection lifecycle (open, reuse, close), and abstracts away database-specific connection protocols, allowing clients to reference databases by logical name rather than managing raw connection strings.","intents":["I want to connect to 5+ databases from a single MCP interface without managing separate credentials for each","I need connection pooling to avoid exhausting database connection limits when an AI agent makes rapid queries","I want to rotate database credentials without redeploying the MCP server or updating client configurations"],"best_for":["Teams managing multiple databases across on-premise and cloud environments","Organizations with strict credential rotation policies requiring centralized secret management","High-throughput AI agent applications where connection pooling prevents resource exhaustion"],"limitations":["Connection pool size must be tuned per database; undersized pools cause query queuing, oversized pools waste memory","Credential rotation requires MCP server restart or hot-reload capability; check if DreamFactory supports zero-downtime credential updates","No built-in connection failover — if primary database is down, queries fail unless manual failover is configured externally","Database-specific connection parameters (timeouts, SSL modes, charset) must be configured per database; no one-size-fits-all defaults"],"requires":["DreamFactory MCP server with database driver support for target databases","Database drivers/libraries installed (JDBC for Java, native drivers for Python, etc.)","Network access from MCP server to all target databases","Credentials for each database stored in DreamFactory's credential store (environment variables, secrets manager, or config file)"],"input_types":["database logical name/identifier","connection parameters (host, port, database name, optional SSL/TLS settings)"],"output_types":["connection status (active, idle, error)","pool statistics (active connections, queued requests)","connection metadata (database type, version, current user)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dreamfactory__cap_2","uri":"capability://data.processing.analysis.schema.introspection.and.dynamic.query.capability.discovery","name":"schema introspection and dynamic query capability discovery","description":"Automatically discovers and exposes database schema information (tables, columns, data types, constraints, relationships) through the MCP interface, allowing clients to dynamically understand what queries are possible without hardcoding schema knowledge. The server introspects the connected databases at startup or on-demand, builds a schema registry, and exposes this metadata via MCP tools/resources, enabling AI agents to construct valid queries based on discovered schema.","intents":["I want Claude to understand my database schema without me manually describing it in prompts","I need to expose a new table to an AI agent and have it automatically available without code changes","I want to generate valid SQL queries dynamically based on discovered schema constraints and relationships"],"best_for":["Teams with frequently changing schemas who want AI agents to adapt without manual updates","Organizations building self-service analytics interfaces where agents explore data based on discovered structure","Developers prototyping AI agents against multiple databases with different schemas"],"limitations":["Schema introspection adds startup latency; large schemas (1000+ tables) may take 10-30 seconds to index","Schema changes require server restart or manual refresh; no real-time schema change detection","Metadata exposure may leak sensitive information (table/column names) to AI agents; requires careful RBAC policy design to hide sensitive schema elements","Complex relationships (circular foreign keys, polymorphic associations) may not be fully represented in schema metadata"],"requires":["DreamFactory MCP server with schema introspection drivers for target databases","Database user with SELECT permissions on information_schema or equivalent system tables","MCP client capable of consuming schema metadata (Claude with tool use, custom agents, etc.)"],"input_types":["database logical name","optional schema filter (specific tables/columns to expose)"],"output_types":["schema metadata (tables, columns, data types, constraints)","relationship metadata (foreign keys, indexes)","column statistics (nullable, default values, max length)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dreamfactory__cap_3","uri":"capability://tool.use.integration.secure.mcp.protocol.tunneling.for.on.premise.database.access","name":"secure mcp protocol tunneling for on-premise database access","description":"Provides secure, encrypted MCP protocol tunneling that allows AI agents running in cloud environments (e.g., Claude API) to safely query on-premise databases without exposing them to the internet. The MCP server acts as a secure gateway, establishing outbound TLS connections to the MCP client, encrypting all traffic, and enforcing authentication/authorization before forwarding database queries to internal systems.","intents":["I have databases behind a corporate firewall and want to let Claude query them without opening firewall ports","I need end-to-end encryption for database queries sent over the internet to cloud-based AI services","I want to audit all database access from external AI agents through a single secure gateway"],"best_for":["Enterprise organizations with strict network security policies and on-premise databases","Teams using cloud-based AI services (Claude API, OpenAI) while maintaining data residency requirements","Regulated industries (healthcare, finance) requiring encrypted, auditable database access from external services"],"limitations":["Requires DreamFactory MCP server to be deployed in a network location with outbound internet access; may conflict with strict egress policies","TLS encryption adds ~20-50ms latency per query due to cryptographic overhead","MCP server becomes a single point of failure for all database access; requires high-availability deployment (load balancing, failover) for production use","Network latency between MCP server and on-premise databases is not optimized; queries across WAN links may be slow"],"requires":["DreamFactory MCP server deployed with network access to on-premise databases","TLS certificates for MCP server (self-signed or CA-signed)","MCP client configuration with server endpoint and authentication credentials","Outbound internet connectivity from MCP server to MCP client (cloud AI service)"],"input_types":["SQL queries","MCP protocol messages (encrypted)"],"output_types":["query results (encrypted in transit)","connection status and audit logs"],"categories":["tool-use-integration","safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dreamfactory__cap_4","uri":"capability://data.processing.analysis.batch.query.execution.with.transaction.support","name":"batch query execution with transaction support","description":"Executes multiple SQL queries in a single MCP request with optional transaction semantics (all-or-nothing atomicity), allowing AI agents to perform multi-step database operations (e.g., insert parent record, then insert child records) without race conditions or partial failures. The server queues queries, optionally wraps them in a database transaction, executes them sequentially, and returns results for each query along with transaction status (committed or rolled back).","intents":["I want an AI agent to insert a customer record and related orders in a single atomic operation without partial failures","I need to execute 10+ queries as a batch to reduce round-trip latency between the agent and database","I want to ensure that if any query in a batch fails, all changes are rolled back automatically"],"best_for":["AI agents performing multi-step data mutations (create, update, delete across related tables)","High-latency environments where reducing round-trips improves performance","Applications requiring strong consistency guarantees (financial transactions, inventory updates)"],"limitations":["Transaction support depends on database isolation level; some databases (MySQL with MyISAM) don't support transactions","Long-running transactions lock rows/tables, potentially blocking other queries; batch size should be limited to prevent deadlocks","Rollback behavior is database-specific; some databases may not fully rollback partial changes if constraints are violated mid-transaction","No built-in retry logic for transient failures; failed batches require manual retry logic in client code"],"requires":["DreamFactory MCP server with transaction support for target database","Database with ACID transaction support (most modern databases)","MCP client capable of sending batch requests (custom implementation or framework support)"],"input_types":["array of SQL query strings","transaction mode flag (atomic/non-atomic)","optional isolation level (READ UNCOMMITTED, READ COMMITTED, etc.)"],"output_types":["array of query results (one per query)","transaction status (committed/rolled back)","error details if any query fails"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dreamfactory__cap_5","uri":"capability://data.processing.analysis.query.result.pagination.and.streaming.for.large.datasets","name":"query result pagination and streaming for large datasets","description":"Handles large query result sets by implementing pagination (offset/limit) and optional streaming (chunked responses) through the MCP protocol, preventing memory exhaustion on both client and server when queries return millions of rows. The server executes queries with cursor-based pagination, returns results in configurable chunk sizes, and allows clients to fetch subsequent pages on-demand without re-executing the full query.","intents":["I want to query a table with 10 million rows without loading all results into memory at once","I need to stream query results to an AI agent in chunks so it can process data incrementally","I want to implement pagination in a web UI backed by database queries through MCP"],"best_for":["Applications querying large datasets (analytics, data exploration, reporting)","Streaming AI agents that process data incrementally rather than all-at-once","Memory-constrained environments (edge devices, serverless functions) where loading full result sets is infeasible"],"limitations":["Cursor-based pagination requires maintaining server-side state (open cursors); long-lived cursors consume database resources","Streaming adds complexity to client code; not all MCP clients support streaming responses","Result ordering must be stable across pages; if underlying data changes between page requests, pagination may skip or duplicate rows","Some databases (e.g., SQL Server) have limited cursor support; pagination implementation may be database-specific"],"requires":["DreamFactory MCP server with pagination/streaming support","MCP client with streaming response support (check protocol version)","Database with cursor or keyset pagination support"],"input_types":["SQL query string","page size (number of rows per chunk)","page offset or cursor token","optional sort order for stable pagination"],"output_types":["paginated result chunk (array of rows)","pagination metadata (total rows, next page token, has more pages)","optional streaming chunks (if streaming mode enabled)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dreamfactory__cap_6","uri":"capability://data.processing.analysis.query.performance.monitoring.and.execution.metrics","name":"query performance monitoring and execution metrics","description":"Captures and exposes database query performance metrics (execution time, rows affected, query plan, index usage) through the MCP interface, allowing clients to understand query efficiency and identify slow queries. The server instruments query execution with timing hooks, optionally captures EXPLAIN plans, and returns metrics alongside results, enabling AI agents and developers to optimize queries or alert on performance regressions.","intents":["I want to see how long each database query takes so I can identify slow operations","I need to understand why a query is slow by seeing the database execution plan","I want to monitor query performance over time and alert if queries degrade"],"best_for":["Teams building performance-sensitive AI applications where query latency matters","Database administrators monitoring AI agent query patterns for optimization opportunities","Developers debugging slow AI workflows to identify database bottlenecks"],"limitations":["EXPLAIN plan capture adds overhead (5-20% latency increase); should be optional for production","Metrics are database-specific; EXPLAIN output format differs between SQL Server, MySQL, PostgreSQL","Metrics don't capture network latency or MCP protocol overhead; only database execution time","Long-term metrics storage requires external monitoring system (Prometheus, CloudWatch, etc.); MCP server doesn't persist metrics"],"requires":["DreamFactory MCP server with query instrumentation enabled","Database user with permissions to run EXPLAIN queries (if plan capture enabled)","Optional: external monitoring system for metrics aggregation and alerting"],"input_types":["SQL query string","optional flag to capture EXPLAIN plan"],"output_types":["query results","execution metrics (execution time in ms, rows affected, rows scanned)","optional EXPLAIN plan (database-specific format)","index usage information (if available)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dreamfactory__cap_7","uri":"capability://safety.moderation.parameterized.query.execution.with.sql.injection.prevention","name":"parameterized query execution with sql injection prevention","description":"Enforces parameterized (prepared) statement execution to prevent SQL injection attacks, requiring clients to provide query templates with placeholders and separate parameter values that are safely bound by the database driver. The MCP server validates that queries use parameterized syntax, rejects raw string concatenation, and ensures parameters are type-checked before execution, preventing malicious SQL from being injected through user-controlled inputs.","intents":["I want to safely pass user-controlled data (from AI agent outputs) to database queries without risk of SQL injection","I need to enforce that all queries use parameterized statements to meet security compliance requirements","I want the database driver to handle type conversion safely rather than relying on string escaping"],"best_for":["Security-conscious teams building AI agents that query databases with untrusted inputs","Organizations with compliance requirements (PCI-DSS, HIPAA) mandating parameterized queries","Teams preventing SQL injection vulnerabilities in AI-generated SQL"],"limitations":["Parameterized queries cannot be used for dynamic table/column names; schema elements must be hardcoded or validated against a whitelist","Some databases have limited parameterized query support for complex operations (e.g., dynamic PIVOT queries in SQL Server)","Parameter binding adds minimal overhead (~1-2ms per query) but is negligible compared to query execution time","Developers must understand parameterized syntax; incorrect parameter binding can still lead to logic errors (e.g., wrong parameter order)"],"requires":["DreamFactory MCP server with parameterized query enforcement enabled","MCP client capable of sending parameterized queries (query template + parameter array)","Database driver with parameterized query support (all modern drivers)"],"input_types":["parameterized query template (e.g., 'SELECT * FROM users WHERE id = ?')","parameter array (values to bind to placeholders)","optional parameter types (for type-safe binding)"],"output_types":["query results","error if query is not properly parameterized"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["DreamFactory MCP server deployed and running","Network connectivity to target databases (on-premise or cloud)","Database credentials with appropriate permissions configured in DreamFactory","MCP client (Claude, Anthropic SDK, or compatible tool) with MCP server configuration","RBAC policies defined in DreamFactory for user roles and database access","DreamFactory MCP server with database driver support for target databases","Database drivers/libraries installed (JDBC for Java, native drivers for Python, etc.)","Network access from MCP server to all target databases","Credentials for each database stored in DreamFactory's credential store (environment variables, secrets manager, or config file)","DreamFactory MCP server with schema introspection drivers for target databases"],"failure_modes":["RBAC enforcement depends on correct policy configuration — misconfigured policies bypass intended restrictions","Query complexity and performance depend on underlying database optimizer; MCP layer adds ~50-100ms overhead per query","No built-in query result caching — repeated queries hit the database each time unless implemented at application layer","Limited to databases with JDBC/ODBC drivers or native connection libraries; custom data sources require custom adapters","Connection pool size must be tuned per database; undersized pools cause query queuing, oversized pools waste memory","Credential rotation requires MCP server restart or hot-reload capability; check if DreamFactory supports zero-downtime credential updates","No built-in connection failover — if primary database is down, queries fail unless manual failover is configured externally","Database-specific connection parameters (timeouts, SSL modes, charset) must be configured per database; no one-size-fits-all defaults","Schema introspection adds startup latency; large schemas (1000+ tables) may take 10-30 seconds to index","Schema changes require server restart or manual refresh; no real-time schema change detection","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:03.038Z","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=dreamfactory","compare_url":"https://unfragile.ai/compare?artifact=dreamfactory"}},"signature":"OGSHbUq1Fmo0bXNcfOmcyNqcjoZ8xdZL3V7QmjVotZryXsaX0a9UmlCHBW13NGpEC8sLsfN+gzAr3uh0BJ+XDQ==","signedAt":"2026-06-22T12:54:17.612Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dreamfactory","artifact":"https://unfragile.ai/dreamfactory","verify":"https://unfragile.ai/api/v1/verify?slug=dreamfactory","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"}}