{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_davidf9999-gx-mcp-server","slug":"davidf9999-gx-mcp-server","name":"Great Expectations Data Quality Server","type":"mcp","url":"https://github.com/davidf9999/gx-mcp-server","page_url":"https://unfragile.ai/davidf9999-gx-mcp-server","categories":["mcp-servers","testing-quality"],"tags":["mcp","model-context-protocol","smithery:davidf9999/gx-mcp-server"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_davidf9999-gx-mcp-server__cap_0","uri":"capability://data.processing.analysis.programmatic.data.quality.checks.execution","name":"programmatic data quality checks execution","description":"This capability allows users to programmatically execute data quality checks by exposing Great Expectations validation rules as callable tools. It utilizes a microservice architecture to handle requests, enabling seamless integration with LLM agents. The server can load datasets from various sources and apply defined validation rules, making it distinct in its ability to automate data validation workflows across different environments.","intents":["How can I automate data quality checks in my data pipeline?","Can I integrate data validation into my LLM agent's workflow?","What is the best way to run Great Expectations checks programmatically?"],"best_for":["data engineers implementing automated data validation workflows"],"limitations":["Limited to data sources supported by Great Expectations; custom integrations may require additional development."],"requires":["Python 3.7+","Great Expectations library installed"],"input_types":["structured data","configuration files"],"output_types":["validation reports","structured data"],"categories":["data-processing-analysis","mcp-servers"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_davidf9999-gx-mcp-server__cap_1","uri":"capability://data.processing.analysis.multi.source.dataset.loading","name":"multi-source dataset loading","description":"This capability enables the server to load datasets from multiple sources, including databases, cloud storage, and local files. It employs a plugin-based architecture to support various data connectors, allowing users to define which sources to access dynamically. This flexibility sets it apart from other tools that may only support limited data sources.","intents":["How can I load data from different sources for validation?","What options do I have for integrating various data sources?","Can I validate data from both local and cloud storage?"],"best_for":["data scientists working with heterogeneous data environments"],"limitations":["Performance may vary based on the data source and network latency."],"requires":["Python 3.7+","Access credentials for data sources"],"input_types":["structured data","configuration files"],"output_types":["loaded datasets","validation reports"],"categories":["data-processing-analysis","integrations"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_davidf9999-gx-mcp-server__cap_2","uri":"capability://tool.use.integration.flexible.authentication.methods","name":"flexible authentication methods","description":"This capability supports multiple authentication methods for accessing data sources, including API keys, OAuth, and basic authentication. It uses a modular authentication framework that allows users to configure their preferred method easily. This flexibility is a key differentiator, as many tools offer limited authentication options.","intents":["What authentication methods can I use to access my data?","How can I secure my data access in automated workflows?","Can I use OAuth for my data source authentication?"],"best_for":["security-conscious developers integrating data sources"],"limitations":["Complexity in setup may increase with custom authentication flows."],"requires":["Python 3.7+","Credentials for chosen authentication method"],"input_types":["authentication tokens","configuration files"],"output_types":["success/failure responses","access tokens"],"categories":["tool-use-integration","security"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_davidf9999-gx-mcp-server__cap_3","uri":"capability://tool.use.integration.transport.mode.flexibility","name":"transport mode flexibility","description":"This capability allows users to choose from multiple transport modes for data transfer, including HTTP, gRPC, and WebSocket. It leverages a transport layer abstraction that enables seamless switching between modes based on user requirements. This design choice enhances performance and reliability, distinguishing it from alternatives with rigid transport options.","intents":["How can I choose the best transport mode for my data transfer?","What options do I have for communicating with my data sources?","Can I use WebSocket for real-time data validation?"],"best_for":["developers looking for efficient data transfer solutions"],"limitations":["Some transport modes may require additional setup or libraries."],"requires":["Python 3.7+","Network configuration for chosen transport mode"],"input_types":["data requests","configuration files"],"output_types":["data responses","validation reports"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_davidf9999-gx-mcp-server__cap_4","uri":"capability://data.processing.analysis.validation.rules.definition.and.management","name":"validation rules definition and management","description":"This capability allows users to define and manage validation rules for their datasets programmatically. It uses a rule-based engine that supports various validation types, enabling users to create complex validation logic. This feature is distinct because it integrates directly with the Great Expectations framework, providing a seamless experience for users familiar with its syntax.","intents":["How can I define custom validation rules for my datasets?","What is the best way to manage data validation logic?","Can I create complex validation rules using Great Expectations?"],"best_for":["data analysts creating custom data validation logic"],"limitations":["Complex validation rules may require deeper knowledge of Great Expectations."],"requires":["Python 3.7+","Great Expectations library installed"],"input_types":["validation rule definitions","configuration files"],"output_types":["validation logic","validation reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":34,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","Great Expectations library installed","Access credentials for data sources","Credentials for chosen authentication method","Network configuration for chosen transport mode"],"failure_modes":["Limited to data sources supported by Great Expectations; custom integrations may require additional development.","Performance may vary based on the data source and network latency.","Complexity in setup may increase with custom authentication flows.","Some transport modes may require additional setup or libraries.","Complex validation rules may require deeper knowledge of Great Expectations.","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.5900000000000001,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:26.345Z","last_scraped_at":"2026-05-03T15:19:46.451Z","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=davidf9999-gx-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=davidf9999-gx-mcp-server"}},"signature":"o7Bio/iJIxu4VKj6TNT7lokgHzkZA3+vThgvoyBCuCdz7+/+iYnipzrKTBlHgMIo+ELx2NhIScxz3L+pWAmvDA==","signedAt":"2026-06-21T01:41:31.090Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/davidf9999-gx-mcp-server","artifact":"https://unfragile.ai/davidf9999-gx-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=davidf9999-gx-mcp-server","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"}}