{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-mcp-toolbox-for-databases","slug":"mcp-toolbox-for-databases","name":"MCP Toolbox for Databases","type":"mcp","url":"https://github.com/googleapis/genai-toolbox","page_url":"https://unfragile.ai/mcp-toolbox-for-databases","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-mcp-toolbox-for-databases__cap_0","uri":"capability://tool.use.integration.multi.database.connection.pooling.with.unified.lifecycle.management","name":"multi-database connection pooling with unified lifecycle management","description":"Manages connection pools across 60+ database source types (PostgreSQL, MySQL, BigQuery, Cloud SQL, Spanner, etc.) through a centralized Source Architecture pattern. Each database type has a dedicated source handler that manages connection lifecycle, credential rotation, and pool sizing. The system maintains persistent connections with automatic reconnection logic and supports both direct connections and cloud-managed database proxies, eliminating the need for applications to implement database-specific connection logic.","intents":["I need to connect multiple AI agents to different databases without managing individual connection pools","I want to safely rotate database credentials without restarting my application","I need connection pooling that works across PostgreSQL, BigQuery, and Cloud SQL simultaneously"],"best_for":["Teams building multi-database AI agents","Enterprise applications requiring centralized database access control","Developers migrating from REST APIs to direct database access"],"limitations":["Connection pool sizing is configured per-source and cannot be dynamically adjusted at runtime without server restart","No built-in connection failover to read replicas — requires manual configuration of replica endpoints as separate sources","Maximum concurrent connections limited by underlying database driver capabilities, not the toolbox itself"],"requires":["Go 1.20+ (for building from source)","Valid database credentials (IAM, OAuth2, or connection strings)","Network access to target databases","For Cloud databases: appropriate GCP service account or IAM roles"],"input_types":["database connection strings","IAM service account JSON","OAuth2 tokens","OIDC credentials"],"output_types":["active database connections","connection pool metrics","connection state telemetry"],"categories":["tool-use-integration","database-connectivity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-toolbox-for-databases__cap_1","uri":"capability://tool.use.integration.mcp.protocol.handler.with.dual.mode.server.operation","name":"mcp protocol handler with dual-mode server operation","description":"Implements the Model Context Protocol (MCP) as a native server transport, enabling seamless integration with MCP-compatible clients (Claude Desktop, Cursor IDE, custom agents). The server operates in two modes: stdio mode for local IDE integration (cmd/root.go --stdio flag) and HTTP server mode for production agent deployments (cmd/root.go --address flag). The MCP Protocol Handler translates between MCP resource/tool requests and internal tool execution, maintaining full protocol compliance while exposing database tools as callable resources.","intents":["I want to use database tools directly in Claude Desktop or Cursor without custom integrations","I need to deploy the same tool definitions to both my IDE and my production AI agent","I want MCP clients to discover and call database tools with full schema validation"],"best_for":["Developers using Claude Desktop or Cursor IDE","Teams standardizing on MCP for AI tool orchestration","Organizations deploying AI agents that need database access"],"limitations":["Stdio mode is single-connection only — cannot serve multiple concurrent MCP clients on the same process","HTTP mode requires manual TLS configuration for production use — no built-in certificate management","MCP resource streaming is limited to tools defined in configuration; dynamic tool generation at request time is not supported"],"requires":["MCP-compatible client (Claude Desktop 0.1.0+, Cursor 0.40+, or custom MCP implementation)","Server running with --stdio or --address flag","Valid tools.yaml configuration file"],"input_types":["MCP CallTool requests","MCP ListTools requests","MCP ListResources requests"],"output_types":["MCP CallToolResult","MCP Tool definitions with JSON schemas","MCP Resource listings"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-toolbox-for-databases__cap_10","uri":"capability://automation.workflow.pre.and.post.processing.hooks.for.custom.tool.logic.and.result.transformation","name":"pre- and post-processing hooks for custom tool logic and result transformation","description":"Provides extensibility through pre-processing hooks (executed before tool invocation) and post-processing hooks (executed after tool invocation) defined in YAML configuration. Pre-processing hooks validate parameters, rewrite queries, or fetch additional context. Post-processing hooks filter results, aggregate data, or transform output format. Hooks are implemented as embedded scripts or external command invocations, allowing custom logic without modifying the core server. This enables tool customization for specific use cases without code changes.","intents":["I want to validate tool parameters before execution (e.g., check user permissions)","I need to transform query results before returning them to agents (e.g., filter sensitive columns)","I want to add custom logic to tools without modifying the server code"],"best_for":["Teams customizing tools for specific business logic","Organizations with complex data governance requirements","Developers extending tools without forking the codebase"],"limitations":["Pre/post-processing hooks add execution latency (50-500ms depending on complexity) — not suitable for ultra-low-latency applications","Hooks are executed synchronously — no support for async operations or external service calls","Hook failures are not retried — a failed hook will fail the entire tool execution"],"requires":["YAML configuration with hook definitions","Hook implementation (embedded script or external command)","Execution environment with required dependencies (Python, Node.js, etc.)"],"input_types":["tool parameters","query results","execution context"],"output_types":["validated/transformed parameters","filtered/transformed results","execution metadata"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-toolbox-for-databases__cap_11","uri":"capability://automation.workflow.cloud.sql.admin.integration.for.database.instance.management.and.provisioning","name":"cloud sql admin integration for database instance management and provisioning","description":"Provides tools for managing Google Cloud SQL instances through the Cloud SQL Admin API, including instance listing, user creation, database provisioning, and backup management. The system authenticates to Cloud SQL Admin using IAM, discovers available instances, and exposes management operations as callable tools. This enables AI agents to provision databases, create users, or manage backups as part of automated workflows. Tools support parameter validation and dry-run modes for safety.","intents":["I want agents to provision Cloud SQL instances as part of infrastructure automation","I need to create database users and manage access through AI workflows","I want to automate Cloud SQL backup and restore operations"],"best_for":["Google Cloud organizations automating database infrastructure","Teams building self-service database provisioning workflows","DevOps teams integrating AI agents with infrastructure management"],"limitations":["Cloud SQL Admin tools are limited to Google Cloud SQL — no support for on-premises or other cloud databases","Instance provisioning is asynchronous — tools return immediately but instance creation may take minutes","User creation requires database-specific credentials — cannot create users without database access"],"requires":["GCP service account with Cloud SQL Admin role","Cloud SQL instances in the same GCP project","Network access to Cloud SQL Admin API (sqladmin.googleapis.com)"],"input_types":["Cloud SQL instance IDs","database names","user credentials","backup configurations"],"output_types":["instance listings (JSON)","instance details and status","operation status (for async operations)","backup listings"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-toolbox-for-databases__cap_12","uri":"capability://planning.reasoning.agent.skills.generation.for.automatic.llm.prompt.optimization","name":"agent skills generation for automatic llm prompt optimization","description":"Automatically generates optimized LLM prompts (agent skills) from tool definitions, including tool descriptions, parameter schemas, and usage examples. The system analyzes tool metadata to create clear, concise prompts that help LLMs understand tool capabilities and constraints. Generated skills can be exported in multiple formats (text, JSON, YAML) for use in different agent frameworks (LangChain, LlamaIndex, Genkit). This reduces manual prompt engineering and ensures consistency across agents.","intents":["I want to automatically generate LLM prompts from my tool definitions","I need to ensure agents understand tool parameters and constraints without manual prompt writing","I want to export tool definitions in formats compatible with my agent framework"],"best_for":["Teams building multiple AI agents with the same tools","Developers reducing manual prompt engineering effort","Organizations standardizing on tool-driven agent development"],"limitations":["Generated prompts are generic and may not be optimal for specific use cases — manual refinement is often needed","Prompt generation does not consider agent-specific context or domain knowledge","Export formats are limited to text, JSON, and YAML — no support for proprietary agent frameworks"],"requires":["Valid tool definitions in tools.yaml","Tool metadata (descriptions, parameter schemas, examples)"],"input_types":["tool definitions","export format specification"],"output_types":["generated prompts (text)","tool definitions (JSON, YAML)","usage examples"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-toolbox-for-databases__cap_13","uri":"capability://automation.workflow.prebuilt.tool.templates.for.common.database.patterns","name":"prebuilt tool templates for common database patterns","description":"Provides pre-configured tool templates for common database operations (list tables, describe schema, count rows, etc.) that can be instantiated with minimal configuration. Templates are defined in internal/prebuiltconfigs/prebuiltconfigs.go and include parameter schemas, execution policies, and result formatting. Users can reference templates in tools.yaml and override specific parameters without redefining entire tools. This accelerates tool development and ensures consistency across common patterns.","intents":["I want to quickly add standard database tools without writing YAML from scratch","I need consistent tool definitions for common operations across multiple databases","I want to reuse tool templates across different projects"],"best_for":["Teams building tools quickly without extensive configuration","Organizations standardizing on common database patterns","Developers new to the toolbox wanting quick-start templates"],"limitations":["Prebuilt templates are limited to common patterns — specialized use cases require custom tool definitions","Template overrides are limited to parameter-level customization — structural changes require custom tools","Templates are hardcoded in the binary — adding new templates requires recompilation"],"requires":["Valid tools.yaml configuration","Reference to prebuilt template names"],"input_types":["template names","parameter overrides"],"output_types":["instantiated tool definitions","compiled tool schemas"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-toolbox-for-databases__cap_2","uri":"capability://automation.workflow.dynamic.tool.definition.loading.and.hot.reloading.from.yaml.configuration","name":"dynamic tool definition loading and hot-reloading from yaml configuration","description":"Loads tool definitions from tools.yaml configuration files at startup and supports dynamic reloading without server restarts. The system parses YAML to define SQL tools, BigQuery tools, Looker tools, and HTTP utilities with parameter schemas, pre/post-processing hooks, and execution policies. Changes to tools.yaml are detected and reloaded at runtime, allowing operators to add new tools, modify parameters, or adjust execution policies without downtime. Tool definitions are compiled into JSON schemas for MCP protocol exposure.","intents":["I want to add new database queries as tools without redeploying the server","I need to modify tool parameters and execution policies on-the-fly for A/B testing","I want to define parameterized SQL queries that agents can call with different inputs"],"best_for":["Operations teams managing AI agent tool catalogs","Developers iterating on tool definitions during development","Organizations with frequent tool requirement changes"],"limitations":["Hot-reloading does not support adding new database sources — only tools within existing sources can be reloaded","YAML parsing errors will silently fail to reload; no validation of tool definitions before applying changes","Tool parameter schema changes do not automatically migrate existing agent prompts or cached tool definitions"],"requires":["Valid tools.yaml file in YAML 1.2 format","Write permissions to configuration file location","File system monitoring support (inotify on Linux, FSEvents on macOS)"],"input_types":["YAML configuration files","Tool parameter schemas (JSON Schema format)","Pre/post-processing script definitions"],"output_types":["Compiled tool definitions","JSON schemas for MCP protocol","Execution policies and parameter constraints"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-toolbox-for-databases__cap_3","uri":"capability://safety.moderation.integrated.iam.oauth2.and.oidc.authentication.with.credential.rotation","name":"integrated iam, oauth2, and oidc authentication with credential rotation","description":"Provides pluggable authentication architecture supporting Google Cloud IAM, OAuth2, and OpenID Connect (OIDC) for secure database access. Credentials are managed through internal/server/config.go (lines 190-198) with automatic token refresh and rotation logic. The system supports service account JSON files, OAuth2 authorization code flows, and OIDC token exchange, enabling fine-grained access control without embedding credentials in configuration. Authentication is decoupled from tool execution, allowing different tools to use different credential sources.","intents":["I want to use Google Cloud IAM roles instead of database passwords for authentication","I need to rotate database credentials automatically without restarting agents","I want to authenticate agents using OIDC tokens from my identity provider"],"best_for":["Google Cloud Platform users leveraging IAM for database access","Enterprise organizations with centralized identity management","Teams requiring credential rotation for compliance"],"limitations":["OAuth2 authorization code flow requires manual user interaction for initial token grant — not suitable for unattended service-to-service authentication","Credential rotation is automatic for OAuth2/OIDC but requires manual refresh for static service account keys","No built-in credential caching — each tool execution may trigger token refresh, adding 100-500ms latency"],"requires":["For IAM: GCP service account with appropriate database roles (Cloud SQL Client, BigQuery Data Editor, etc.)","For OAuth2: OAuth2 client credentials and redirect URI configuration","For OIDC: OIDC provider URL and client credentials","Network access to identity provider endpoints"],"input_types":["service account JSON files","OAuth2 client ID and secret","OIDC provider URLs","credential refresh tokens"],"output_types":["authenticated database connections","access tokens with expiration metadata","credential rotation events (telemetry)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-toolbox-for-databases__cap_4","uri":"capability://data.processing.analysis.sql.tool.execution.with.parameterized.query.templates.and.result.formatting","name":"sql tool execution with parameterized query templates and result formatting","description":"Executes parameterized SQL queries against relational databases (PostgreSQL, MySQL, SQL Server) through a template-based tool system. Tools are defined with parameter schemas that map to SQL placeholders, preventing SQL injection through strict parameter binding. Results are formatted as structured data (JSON, CSV, or raw rows) with configurable row limits and timeout policies. The system supports pre-processing (parameter validation, query rewriting) and post-processing (result filtering, aggregation) hooks defined in YAML.","intents":["I want agents to query my database safely without risk of SQL injection","I need to expose specific database queries as tools with parameter validation","I want to limit query results and execution time to prevent resource exhaustion"],"best_for":["Teams building AI agents that need database query access","Organizations with strict SQL injection prevention requirements","Developers creating parameterized query APIs for LLM agents"],"limitations":["Parameterized queries cannot use dynamic table/column names — only values can be parameterized, limiting flexibility for schema exploration tools","Result formatting is limited to JSON, CSV, and raw rows — no support for custom serialization formats","Query timeout is enforced at the server level but may not interrupt long-running queries on the database side (depends on driver support)"],"requires":["Valid SQL query template with named parameters (e.g., :user_id, :start_date)","Parameter schema definition in JSON Schema format","Active database connection (via connection pooling capability)","Database user with SELECT/INSERT/UPDATE/DELETE permissions as needed"],"input_types":["SQL query templates","parameter values matching schema","execution options (timeout, row limit)"],"output_types":["JSON array of result rows","CSV formatted results","raw database rows with metadata"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-toolbox-for-databases__cap_5","uri":"capability://data.processing.analysis.bigquery.specific.tools.with.dataset.table.discovery.and.query.optimization","name":"bigquery-specific tools with dataset/table discovery and query optimization","description":"Provides specialized tools for BigQuery including dataset listing, table schema inspection, and optimized query execution. Tools leverage BigQuery's native APIs to discover available datasets and tables, expose schema information to agents, and execute queries with automatic cost estimation. The system handles BigQuery-specific features like partitioning, clustering, and query caching, exposing these as tool parameters. Results are streamed and formatted as JSON with automatic pagination for large result sets.","intents":["I want agents to discover available BigQuery datasets and tables without hardcoding schema information","I need to expose BigQuery schema inspection as a tool so agents can understand data structure","I want to execute BigQuery queries with cost estimation before running them"],"best_for":["Google Cloud organizations using BigQuery as their data warehouse","Teams building AI agents that need to explore BigQuery schemas dynamically","Data teams wanting to expose BigQuery access to AI applications"],"limitations":["Dataset discovery is limited to datasets accessible by the service account — no support for cross-project dataset access without additional IAM configuration","Query cost estimation is approximate and may not match actual billing due to BigQuery's dynamic pricing","Schema inspection does not include table statistics (row count, size) — requires separate INFORMATION_SCHEMA queries"],"requires":["GCP service account with BigQuery Data Viewer and BigQuery Job User roles","BigQuery project ID and dataset names","Network access to BigQuery APIs (bigquery.googleapis.com)"],"input_types":["BigQuery project ID","dataset names","SQL queries (BigQuery dialect)","query parameters"],"output_types":["dataset listings (JSON)","table schemas (JSON with column types)","query results (JSON with pagination)","cost estimates (in USD)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-toolbox-for-databases__cap_6","uri":"capability://data.processing.analysis.looker.integration.for.bi.tool.access.and.saved.query.execution","name":"looker integration for bi tool access and saved query execution","description":"Integrates with Looker instances to expose saved looks, dashboards, and explores as callable tools. The system authenticates to Looker using OAuth2 or API keys, discovers available content, and executes saved queries with parameter substitution. Results are returned as structured data (JSON) with metadata about the underlying Looker model. This enables agents to access BI insights without direct database access, leveraging Looker's data modeling and access control.","intents":["I want agents to run Looker saved queries without direct database access","I need to expose Looker dashboards and explores as tools for AI agents","I want agents to access BI insights through Looker's data models instead of raw SQL"],"best_for":["Organizations with Looker deployments that want AI agent integration","Teams preferring BI tool access over direct database access for agents","Enterprises with complex data models defined in Looker"],"limitations":["Looker integration requires valid Looker instance URL and API credentials — no support for Looker Cloud without additional configuration","Saved query execution is limited to queries that return structured data — dashboard rendering is not supported","Parameter substitution only works for Looker filters defined in the saved look — dynamic parameter injection is not supported"],"requires":["Active Looker instance (Looker 7.0+)","Looker API credentials (OAuth2 or API key)","Looker user account with access to target looks/explores","Network access to Looker instance"],"input_types":["Looker look/explore IDs","filter parameters","result format options"],"output_types":["structured query results (JSON)","Looker model metadata","look/explore definitions"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-toolbox-for-databases__cap_7","uri":"capability://tool.use.integration.http.and.utility.tools.for.external.api.integration.and.data.transformation","name":"http and utility tools for external api integration and data transformation","description":"Provides generic HTTP tools for calling external APIs and utility tools for data transformation (JSON parsing, CSV conversion, string manipulation). HTTP tools support GET, POST, PUT, DELETE with configurable headers, authentication, and request/response transformation. Utility tools enable agents to transform query results or API responses without additional processing steps. All tools support parameter schemas and pre/post-processing hooks for custom logic.","intents":["I want agents to call external APIs as part of database workflows","I need to transform query results into different formats (JSON to CSV, etc.)","I want to enrich database queries with data from external APIs"],"best_for":["Teams building multi-system AI workflows","Developers needing data transformation without external services","Organizations integrating multiple data sources"],"limitations":["HTTP tools do not support streaming responses — entire response must fit in memory","No built-in retry logic for failed HTTP requests — requires custom pre-processing hooks","Utility tools are limited to basic transformations — complex data processing requires external services"],"requires":["Valid HTTP endpoint URLs","API credentials (if required)","Network access to external APIs","Request/response schema definitions"],"input_types":["HTTP method (GET, POST, etc.)","request headers and body","transformation specifications"],"output_types":["HTTP response body (JSON, text, etc.)","transformed data (JSON, CSV, etc.)","HTTP status codes and metadata"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-toolbox-for-databases__cap_8","uri":"capability://automation.workflow.opentelemetry.integration.for.distributed.tracing.and.observability","name":"opentelemetry integration for distributed tracing and observability","description":"Provides built-in OpenTelemetry instrumentation for distributed tracing, metrics, and logging across all tool executions. The system exports traces to OpenTelemetry-compatible backends (Jaeger, Datadog, Google Cloud Trace) with automatic span creation for tool calls, database queries, and authentication events. Metrics include tool execution latency, error rates, and connection pool utilization. Telemetry is configured through internal/server/config.go (lines 61-66) and can be enabled/disabled per environment.","intents":["I want to trace tool execution across my AI agent and database systems","I need to monitor tool performance and identify bottlenecks","I want to debug agent behavior by inspecting detailed execution traces"],"best_for":["Production AI agent deployments requiring observability","Teams using OpenTelemetry-compatible monitoring platforms","Organizations with strict audit and compliance requirements"],"limitations":["Telemetry export adds 50-200ms latency per request (depends on backend) — not suitable for ultra-low-latency applications","Trace sampling must be configured manually — no automatic adaptive sampling based on error rates","Sensitive data (query parameters, results) may be included in traces — requires careful configuration to avoid data leaks"],"requires":["OpenTelemetry collector or backend (Jaeger, Datadog, Google Cloud Trace, etc.)","OTEL_EXPORTER_OTLP_ENDPOINT environment variable or configuration","Network access to telemetry backend"],"input_types":["tool execution events","database query events","authentication events"],"output_types":["distributed traces (OpenTelemetry format)","metrics (Prometheus format)","structured logs (JSON)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-toolbox-for-databases__cap_9","uri":"capability://tool.use.integration.resource.manager.for.exposing.database.schemas.and.tool.definitions.as.mcp.resources","name":"resource manager for exposing database schemas and tool definitions as mcp resources","description":"Implements the MCP Resource Manager to expose database schemas, tool definitions, and configuration metadata as discoverable resources. Resources are organized hierarchically (e.g., /databases/postgres/schemas/public/tables/users) and support both static resources (pre-defined tools) and dynamic resources (discovered database objects). Clients can list resources, read resource contents, and subscribe to resource updates. This enables IDE integration where database schemas appear as browsable resources.","intents":["I want to browse database schemas in my IDE as MCP resources","I need agents to discover available tools and database objects dynamically","I want to expose database metadata (tables, columns, indexes) as readable resources"],"best_for":["IDE integrations (Claude Desktop, Cursor) requiring schema browsing","AI agents needing dynamic discovery of available tools and data","Developers building MCP clients that consume database metadata"],"limitations":["Resource discovery is limited to objects accessible by the service account — no support for role-based resource filtering","Dynamic resource generation (e.g., discovering all tables) may be slow for large schemas — no caching of resource listings","Resource subscriptions are not supported — clients must poll for updates"],"requires":["MCP-compatible client supporting resource listing","Active database connections with schema inspection permissions","Valid tools.yaml configuration defining resource structure"],"input_types":["resource path queries","resource type filters"],"output_types":["resource listings (MCP format)","resource contents (JSON, YAML, or text)","resource metadata (MIME type, size, etc.)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Go 1.20+ (for building from source)","Valid database credentials (IAM, OAuth2, or connection strings)","Network access to target databases","For Cloud databases: appropriate GCP service account or IAM roles","MCP-compatible client (Claude Desktop 0.1.0+, Cursor 0.40+, or custom MCP implementation)","Server running with --stdio or --address flag","Valid tools.yaml configuration file","YAML configuration with hook definitions","Hook implementation (embedded script or external command)","Execution environment with required dependencies (Python, Node.js, etc.)"],"failure_modes":["Connection pool sizing is configured per-source and cannot be dynamically adjusted at runtime without server restart","No built-in connection failover to read replicas — requires manual configuration of replica endpoints as separate sources","Maximum concurrent connections limited by underlying database driver capabilities, not the toolbox itself","Stdio mode is single-connection only — cannot serve multiple concurrent MCP clients on the same process","HTTP mode requires manual TLS configuration for production use — no built-in certificate management","MCP resource streaming is limited to tools defined in configuration; dynamic tool generation at request time is not supported","Pre/post-processing hooks add execution latency (50-500ms depending on complexity) — not suitable for ultra-low-latency applications","Hooks are executed synchronously — no support for async operations or external service calls","Hook failures are not retried — a failed hook will fail the entire tool execution","Cloud SQL Admin tools are limited to Google Cloud SQL — no support for on-premises or other cloud databases","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"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":"2026-06-17T09:51:03.578Z","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=mcp-toolbox-for-databases","compare_url":"https://unfragile.ai/compare?artifact=mcp-toolbox-for-databases"}},"signature":"ANtv2TWeDcTx2zuzM1GEM9jHKgWIqEpeUso2SRJB3UEbj7/3Bc2GXnqWEjKT0zIA+9SkVR27ypdOLCTMTeBsBw==","signedAt":"2026-06-22T01:10:02.990Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-toolbox-for-databases","artifact":"https://unfragile.ai/mcp-toolbox-for-databases","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-toolbox-for-databases","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"}}