{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-dataworks","slug":"dataworks","name":"DataWorks","type":"mcp","url":"https://github.com/aliyun/alibabacloud-dataworks-mcp-server","page_url":"https://unfragile.ai/dataworks","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-dataworks__cap_0","uri":"capability://tool.use.integration.mcp.protocol.compliant.tool.registration.and.schema.conversion","name":"mcp protocol-compliant tool registration and schema conversion","description":"Converts ActionTool definitions into Model Context Protocol (MCP) compliant tool schemas and registers them with the MCP server via @modelcontextprotocol/sdk. The system maintains a bidirectional mapping between internal ActionTool representations and MCP tool schemas, enabling AI clients (Cursor, Cline, etc.) to discover and invoke DataWorks operations through standardized MCP protocol messages. Schema conversion handles parameter validation, type mapping, and response formatting according to MCP specification.","intents":["Enable AI agents to discover available DataWorks operations through MCP protocol","Expose DataWorks API capabilities as callable tools to MCP-compatible clients","Maintain schema consistency between internal tool definitions and MCP protocol contracts"],"best_for":["AI agent developers building MCP-compatible clients (Cursor, Cline, Claude Desktop)","Teams integrating DataWorks into AI-driven automation workflows","Developers building multi-tool orchestration systems using MCP"],"limitations":["Schema conversion adds ~50-100ms overhead per tool registration during server startup","MCP protocol limits tool descriptions to 1024 characters, truncating complex operation documentation","No built-in versioning for tool schemas — breaking changes require server restart"],"requires":["Node.js v16 or higher","@modelcontextprotocol/sdk package","MCP-compatible client implementation"],"input_types":["ActionTool definitions (JSON schema format)","Tool metadata (name, description, parameters)"],"output_types":["MCP tool schema (JSON)","Tool availability list (JSON array)"],"categories":["tool-use-integration","protocol-compliance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dataworks__cap_1","uri":"capability://tool.use.integration.alibaba.cloud.credential.resolution.and.authentication.management","name":"alibaba cloud credential resolution and authentication management","description":"Abstracts Alibaba Cloud authentication through @alicloud/credentials package, supporting multiple credential sources (Access Key/Secret Key, STS tokens, environment variables) with automatic fallback chain resolution. The OpenApiClient.createClient() factory in src/openApiClient/index.ts handles credential initialization, endpoint selection (production vs pre-release), and regional configuration via ALIBABA_CLOUD_ACCESS_KEY_ID, ALIBABA_CLOUD_ACCESS_KEY_SECRET, and REGION environment variables. Credentials are resolved once at server startup and reused across all subsequent API calls.","intents":["Authenticate with Alibaba Cloud DataWorks APIs without hardcoding credentials","Support multiple authentication methods (AK/SK, STS tokens) transparently","Configure regional endpoints and pre-release API access dynamically"],"best_for":["DevOps teams deploying DataWorks MCP server in containerized environments","Organizations using Alibaba Cloud credential management systems","Multi-region DataWorks deployments requiring dynamic endpoint configuration"],"limitations":["Credentials are resolved once at startup — rotating credentials requires server restart","No built-in credential caching or refresh mechanism for STS tokens with expiration","Environment variable configuration is global — no per-tool credential overrides"],"requires":["Node.js v16 or higher","@alicloud/credentials package","Valid Alibaba Cloud Access Key ID and Secret Key, or STS token","Environment variables: ALIBABA_CLOUD_ACCESS_KEY_ID, ALIBABA_CLOUD_ACCESS_KEY_SECRET, REGION (optional)"],"input_types":["Environment variables (string)","Credential configuration objects"],"output_types":["Authenticated OpenAPI client instance","Regional endpoint URL (string)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dataworks__cap_10","uri":"capability://data.processing.analysis.api.type.definitions.and.response.schema.management","name":"api type definitions and response schema management","description":"The system maintains comprehensive API type definitions and response schemas for DataWorks operations in src/types/ and related modules. These definitions include request/response types, error codes, status enumerations, and complex nested object structures. Type definitions are used for parameter validation, response parsing, and schema generation. The system provides TypeScript type safety for API interactions and enables IDE autocompletion for developers extending the server. Response schemas are used to normalize API responses into consistent formats for MCP clients.","intents":["Provide TypeScript type safety for DataWorks API interactions","Enable IDE autocompletion and type checking for tool parameters and responses","Normalize API responses into consistent formats for MCP clients"],"best_for":["TypeScript developers extending DataWorks MCP server with custom tools","Teams requiring type safety and IDE support for API integrations","Organizations maintaining large codebases with multiple API operations"],"limitations":["Type definitions must be manually maintained as DataWorks API evolves","No automatic type generation from API specifications — definitions are static","Complex nested types may not fully capture API response structures"],"requires":["Node.js v16 or higher","TypeScript 4.0+ (for type checking)","API type definition files"],"input_types":["API response objects (JSON)"],"output_types":["Typed response objects (TypeScript)","Validated response data (JSON)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dataworks__cap_2","uri":"capability://tool.use.integration.generalized.openapi.request.execution.with.parameter.validation.and.transformation","name":"generalized openapi request execution with parameter validation and transformation","description":"The callTool function in src/tools/callTool.ts provides a unified execution engine for DataWorks OpenAPI operations. It validates input parameters against tool schemas, transforms parameters according to API requirements, constructs HTTP requests with proper headers and authentication, executes requests via the authenticated OpenAPI client, and normalizes responses into consistent output formats. The engine handles error propagation, response parsing, and type coercion for complex parameter types (arrays, nested objects, enums).","intents":["Execute arbitrary DataWorks API operations through a single, parameterized interface","Validate tool inputs against schema constraints before API invocation","Transform user-provided parameters into DataWorks API-compatible formats"],"best_for":["AI agents executing dynamic DataWorks operations without hardcoded API logic","Developers building generic API orchestration layers over DataWorks","Teams requiring consistent error handling and response normalization across multiple API operations"],"limitations":["Parameter validation adds ~20-50ms latency per API call for complex schemas","No built-in request batching — each tool invocation triggers a separate HTTP request","Response transformation is lossy for nested objects with unknown schema — fields may be dropped"],"requires":["Node.js v16 or higher","Authenticated OpenAPI client instance","Tool schema definition with parameter specifications","Valid DataWorks API endpoint access"],"input_types":["Tool parameters (JSON object)","Tool schema definition (JSON schema format)"],"output_types":["API response (JSON)","Error object with code and message (JSON)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dataworks__cap_3","uri":"capability://tool.use.integration.tool.initialization.and.dynamic.actiontool.registry.management","name":"tool initialization and dynamic actiontool registry management","description":"The initDataWorksTools() and initExtraTools() functions in src/index.ts populate an ActionTool registry by loading tool definitions from configuration sources and external data sources. The system maintains an in-memory registry of available tools with their schemas, descriptions, and execution handlers. Tool definitions are loaded at server startup and made available to the MCP protocol handler for registration. The registry supports both built-in DataWorks tools and extensible custom tools through the extra tools initialization pipeline.","intents":["Populate the tool registry with DataWorks operations at server startup","Support extensible tool definitions through external configuration sources","Maintain a centralized registry of available tools for MCP discovery"],"best_for":["Developers extending DataWorks MCP server with custom tools","Teams managing large tool inventories across multiple DataWorks regions","Organizations requiring dynamic tool registration without code changes"],"limitations":["Tool registry is static after server startup — adding new tools requires server restart","No built-in tool versioning or deprecation mechanism","Tool loading from external sources adds startup latency proportional to configuration size"],"requires":["Node.js v16 or higher","Tool configuration files or external data sources","ActionTool schema definitions"],"input_types":["Tool configuration (JSON)","External data source definitions"],"output_types":["ActionTool registry (in-memory map)","Tool metadata (name, schema, handler)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dataworks__cap_4","uri":"capability://tool.use.integration.standard.i.o.transport.for.mcp.protocol.communication","name":"standard i/o transport for mcp protocol communication","description":"The MCP server uses StdioServerTransport from @modelcontextprotocol/sdk to handle bidirectional communication with MCP clients over standard input/output streams. This transport mechanism enables the server to receive tool invocation requests as JSON-RPC messages on stdin and send responses and tool results on stdout, making the server compatible with any MCP client that supports stdio-based communication. The transport is initialized in src/mcp/index.ts and manages message framing, serialization, and protocol state.","intents":["Enable stdio-based communication between MCP clients and DataWorks server","Support MCP clients that use stdio transport (Cursor, Cline, Claude Desktop)","Provide lightweight, process-based integration without network overhead"],"best_for":["Local development environments using MCP-compatible IDEs (Cursor, VS Code with extensions)","Containerized deployments where stdio communication is preferred over network sockets","Teams building AI agents that communicate with DataWorks through MCP protocol"],"limitations":["Stdio transport is single-threaded — concurrent requests are queued sequentially","No built-in message buffering — large responses may cause stdout blocking","Process-based communication adds ~5-10ms latency per round-trip vs network sockets"],"requires":["Node.js v16 or higher","@modelcontextprotocol/sdk package","MCP client with stdio transport support"],"input_types":["JSON-RPC messages (stdin)"],"output_types":["JSON-RPC responses (stdout)","Tool execution results (stdout)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dataworks__cap_5","uri":"capability://data.processing.analysis.action.type.schema.conversion.and.parameter.mapping","name":"action type schema conversion and parameter mapping","description":"The system converts DataWorks API action types into standardized tool schemas with parameter definitions, type constraints, and validation rules. This conversion happens in the tool initialization pipeline and maps API operation parameters (required/optional, type, constraints) into MCP-compatible JSON schema format. The conversion handles complex types (arrays, nested objects, enums) and generates human-readable parameter descriptions for AI agents. Schema conversion enables AI clients to understand parameter requirements without consulting API documentation.","intents":["Convert DataWorks API action definitions into AI-understandable tool schemas","Generate parameter constraints and validation rules for AI agents","Enable AI agents to construct valid API requests without manual documentation lookup"],"best_for":["AI agents that need to understand parameter requirements for DataWorks operations","Developers building schema-driven API orchestration systems","Teams requiring consistent parameter validation across multiple API operations"],"limitations":["Schema conversion is lossy for complex nested types — some API constraints may not map to JSON schema","Enum values must be explicitly defined in action types — implicit enums are not detected","Parameter descriptions are limited to 512 characters in MCP protocol"],"requires":["Node.js v16 or higher","Action type definitions with parameter specifications","JSON schema validation library"],"input_types":["Action type definitions (JSON)","Parameter specifications (JSON schema)"],"output_types":["MCP tool schema (JSON schema format)","Parameter validation rules (JSON)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dataworks__cap_6","uri":"capability://data.processing.analysis.external.data.source.integration.for.tool.and.configuration.loading","name":"external data source integration for tool and configuration loading","description":"The system supports loading tool definitions and configuration from external data sources beyond built-in definitions. The architecture in src/tools/ and configuration modules enables pluggable data source adapters that can fetch tool definitions, action types, and system constants from remote APIs, databases, or configuration files. External data sources are loaded during server initialization and merged into the tool registry, enabling dynamic tool discovery without code changes. The system maintains a separation between data source adapters and tool initialization logic.","intents":["Load tool definitions from external sources (APIs, databases, configuration services)","Support dynamic tool discovery without server code changes","Enable centralized tool management across multiple DataWorks MCP server instances"],"best_for":["Organizations managing tool definitions in centralized configuration services","Teams requiring dynamic tool updates without server restarts","Multi-region deployments where tool definitions are managed centrally"],"limitations":["External data source loading adds startup latency proportional to source response time","No built-in caching for external data sources — each server startup fetches fresh definitions","Failures in external data source loading may prevent server startup if not handled gracefully"],"requires":["Node.js v16 or higher","External data source implementation (API, database, configuration service)","Network connectivity to external data sources"],"input_types":["External data source configuration (URL, credentials, format)","Tool definition format (JSON)"],"output_types":["Tool definitions (JSON)","Configuration data (JSON)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dataworks__cap_7","uri":"capability://tool.use.integration.request.routing.and.tool.execution.dispatch","name":"request routing and tool execution dispatch","description":"The MCP server implementation in src/mcp/index.ts routes incoming tool invocation requests to appropriate execution handlers based on tool name and parameters. The routing logic dispatches requests to the callTool execution engine, which validates parameters, constructs API requests, and returns results. The router maintains a mapping between MCP tool names and internal tool handlers, enabling the server to execute any registered tool without explicit routing code. Error handling and response formatting are applied uniformly across all routed requests.","intents":["Route MCP tool invocation requests to appropriate execution handlers","Execute tools with validated parameters and consistent error handling","Maintain separation between protocol handling and tool execution logic"],"best_for":["MCP server implementations requiring dynamic request routing","Developers building tool orchestration systems with multiple execution backends","Teams requiring consistent error handling and response formatting across tools"],"limitations":["Routing adds ~5-10ms latency per request for handler lookup and dispatch","No built-in request queuing or rate limiting — all requests are executed immediately","Tool execution is synchronous — long-running operations block the request handler"],"requires":["Node.js v16 or higher","Tool registry with handler mappings","MCP protocol handler implementation"],"input_types":["MCP tool invocation request (JSON-RPC)","Tool name (string)","Tool parameters (JSON object)"],"output_types":["Tool execution result (JSON)","Error response (JSON)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dataworks__cap_8","uri":"capability://data.processing.analysis.system.constants.and.utility.functions.for.api.operations","name":"system constants and utility functions for api operations","description":"The system provides centralized system constants and utility functions in src/utils/ and src/constants/ modules that support API operations across the server. These include region mappings, endpoint configurations, API response type definitions, error codes, and helper functions for parameter transformation and response parsing. Utilities are shared across tool execution, schema conversion, and external data source loading, reducing code duplication and ensuring consistency. Constants are loaded at server startup and available to all components.","intents":["Provide centralized configuration for regions, endpoints, and API constants","Offer utility functions for parameter transformation and response parsing","Ensure consistent error handling and response formatting across all API operations"],"best_for":["Developers extending DataWorks MCP server with custom tools","Teams maintaining consistency across multiple API operations","Organizations requiring centralized configuration management"],"limitations":["Constants are loaded at startup — changes require server restart","Utility functions are synchronous — no async helper support","No built-in versioning for constants — API changes may break existing tools"],"requires":["Node.js v16 or higher","System constants definitions (JSON)","Utility function implementations"],"input_types":["Region identifiers (string)","API response objects (JSON)"],"output_types":["Endpoint URLs (string)","Transformed parameters (JSON)","Parsed responses (JSON)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dataworks__cap_9","uri":"capability://tool.use.integration.regional.endpoint.configuration.and.dynamic.endpoint.selection","name":"regional endpoint configuration and dynamic endpoint selection","description":"The OpenAPI client supports dynamic endpoint selection based on region configuration and environment variables. The system maintains mappings of Alibaba Cloud regions to production and pre-release DataWorks endpoints (dataworks.{region}.aliyuncs.com and dataworks-pre.{region}.aliyuncs.com). Endpoint selection is determined at client initialization time via the REGION environment variable, enabling multi-region deployments without code changes. The system validates region identifiers and falls back to default regions if invalid values are provided.","intents":["Support multi-region DataWorks deployments with dynamic endpoint selection","Enable pre-release API access through endpoint configuration","Configure regional endpoints without hardcoding region-specific logic"],"best_for":["Organizations with DataWorks deployments across multiple Alibaba Cloud regions","Teams testing pre-release DataWorks APIs in staging environments","Multi-region AI agent deployments requiring region-specific API access"],"limitations":["Endpoint selection is static after client initialization — changing regions requires server restart","No built-in endpoint validation — invalid regions may cause API failures","Pre-release endpoints may have different API contracts than production endpoints"],"requires":["Node.js v16 or higher","Valid Alibaba Cloud region identifier","Environment variable: REGION (optional, defaults to cn-beijing)"],"input_types":["Region identifier (string, e.g., 'cn-beijing', 'cn-shanghai')"],"output_types":["Endpoint URL (string)","API base URL (string)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Node.js v16 or higher","@modelcontextprotocol/sdk package","MCP-compatible client implementation","@alicloud/credentials package","Valid Alibaba Cloud Access Key ID and Secret Key, or STS token","Environment variables: ALIBABA_CLOUD_ACCESS_KEY_ID, ALIBABA_CLOUD_ACCESS_KEY_SECRET, REGION (optional)","TypeScript 4.0+ (for type checking)","API type definition files","Authenticated OpenAPI client instance","Tool schema definition with parameter specifications"],"failure_modes":["Schema conversion adds ~50-100ms overhead per tool registration during server startup","MCP protocol limits tool descriptions to 1024 characters, truncating complex operation documentation","No built-in versioning for tool schemas — breaking changes require server restart","Credentials are resolved once at startup — rotating credentials requires server restart","No built-in credential caching or refresh mechanism for STS tokens with expiration","Environment variable configuration is global — no per-tool credential overrides","Type definitions must be manually maintained as DataWorks API evolves","No automatic type generation from API specifications — definitions are static","Complex nested types may not fully capture API response structures","Parameter validation adds ~20-50ms latency per API call for complex schemas","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"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=dataworks","compare_url":"https://unfragile.ai/compare?artifact=dataworks"}},"signature":"S9QzavsODeCERYqIrRJzUecbVIkdzp3zTIbCZeuGQ0Jj8KeGBi6ZDoZvw8hWOpS9odXTEjkE70Y7f5AyBpT3CQ==","signedAt":"2026-06-22T02:28:31.419Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dataworks","artifact":"https://unfragile.ai/dataworks","verify":"https://unfragile.ai/api/v1/verify?slug=dataworks","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"}}