{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-open-metadata-openmetadata","slug":"mcp-open-metadata-openmetadata","name":"OpenMetadata","type":"repo","url":"https://github.com/open-metadata/OpenMetadata","page_url":"https://unfragile.ai/mcp-open-metadata-openmetadata","categories":["data-pipelines"],"tags":["data-catalog","data-collaboration","data-contracts","data-discovery","data-governance","data-lineage","data-observability","data-profiling","data-quality","data-quality-checks","data-validation","datadiscovery","dataengineering","dataquality","hacktoberfest","mcp","mcp-server","metadata","metadata-management"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-open-metadata-openmetadata__cap_0","uri":"capability://data.processing.analysis.multi.source.metadata.ingestion.with.connector.framework","name":"multi-source metadata ingestion with connector framework","description":"OpenMetadata ingests metadata from 50+ data sources (databases, data warehouses, BI tools, data lakes, pipelines) through a pluggable connector architecture. Each connector implements a standardized extraction interface that maps source-specific metadata schemas to OpenMetadata's unified entity model, with support for incremental ingestion, scheduling via Airflow, and automatic lineage extraction during the ingestion process.","intents":["I need to automatically discover and catalog all tables, columns, and schemas across my Snowflake, BigQuery, and Redshift warehouses","I want to ingest metadata from Tableau, Looker, and Power BI dashboards to understand data dependencies","I need to extract lineage information from Airflow DAGs, dbt projects, and Spark pipelines during metadata collection"],"best_for":["data engineering teams managing multi-warehouse environments","data governance teams building centralized metadata catalogs","organizations migrating from manual metadata management to automated discovery"],"limitations":["Connector coverage varies by source — some sources have basic extraction only, others support full lineage","Incremental ingestion requires source-specific change tracking capabilities; not all sources support efficient delta extraction","Scheduling depends on Airflow availability — requires separate Airflow deployment for production scheduling","Custom connector development requires understanding OpenMetadata's Python SDK and entity model"],"requires":["Python 3.9+ for ingestion framework","Source system credentials and network access","Apache Airflow 2.0+ for scheduled ingestion (optional but recommended)","OpenMetadata backend service running with API access"],"input_types":["database connection strings","API credentials for BI/pipeline tools","Airflow DAG definitions","dbt manifest files"],"output_types":["structured metadata entities (Table, Column, Database, Schema)","lineage relationships (upstream/downstream dependencies)","profiling statistics and data quality metrics"],"categories":["data-processing-analysis","metadata-ingestion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-open-metadata-openmetadata__cap_1","uri":"capability://data.processing.analysis.column.level.lineage.tracking.and.visualization","name":"column-level lineage tracking and visualization","description":"OpenMetadata tracks data lineage at column granularity by parsing transformation logic from SQL, dbt, Spark, and pipeline definitions, building a directed acyclic graph (DAG) of column dependencies across tables and systems. The lineage engine reconstructs column-to-column transformations, enabling impact analysis and root cause investigation across the entire data stack with interactive UI visualization.","intents":["I need to understand which upstream columns feed into a specific metric in my BI tool","I want to trace the impact of a schema change across all downstream tables and dashboards","I need to identify the source of data quality issues by tracing a column back to its origin"],"best_for":["data teams debugging data quality issues across complex pipelines","governance teams performing impact analysis before schema changes","organizations with SQL-heavy or dbt-based transformation logic"],"limitations":["Lineage extraction accuracy depends on SQL parser capabilities — complex CTEs, dynamic SQL, and procedural logic may not be fully resolved","Requires explicit lineage metadata from sources (dbt manifest, Airflow task dependencies); implicit lineage from unstructured code is not extracted","Cross-system lineage (e.g., Kafka → Spark → Snowflake) requires connectors for each system to be configured","Real-time lineage updates depend on source system's change notification capabilities"],"requires":["dbt manifest files or Airflow DAG definitions for transformation logic","SQL parser support for source dialect (Snowflake, BigQuery, PostgreSQL, etc.)","Metadata for upstream and downstream systems already ingested into OpenMetadata"],"input_types":["SQL transformation queries","dbt manifest.json files","Airflow task dependency graphs","Spark job definitions"],"output_types":["lineage graph (nodes = columns/tables, edges = dependencies)","impact analysis reports","interactive lineage visualization in UI"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-open-metadata-openmetadata__cap_10","uri":"capability://tool.use.integration.java.sdk.for.programmatic.metadata.access.and.manipulation","name":"java sdk for programmatic metadata access and manipulation","description":"OpenMetadata provides a Java SDK that enables developers to programmatically query, create, and update metadata entities, execute lineage analysis, and manage access control. The SDK handles authentication, serialization, and API communication, providing a type-safe interface to the OpenMetadata REST API with support for batch operations and streaming responses.","intents":["I want to build a custom data discovery tool that queries OpenMetadata metadata programmatically","I need to bulk update ownership and descriptions for 1000+ tables using a Java script","I want to integrate OpenMetadata metadata into my data pipeline orchestration tool"],"best_for":["Java/JVM developers building custom metadata tools","teams integrating OpenMetadata into existing Java applications","organizations with complex metadata manipulation workflows"],"limitations":["Java SDK only; no native Python, Go, or Node.js SDKs (Python SDK exists but is separate)","SDK version must match OpenMetadata backend version; breaking changes between versions","Batch operations are not atomic; partial failures require manual retry logic","No built-in caching; repeated queries hit the API each time"],"requires":["Java 11+","Maven or Gradle for dependency management","OpenMetadata backend with API access and authentication configured"],"input_types":["entity queries (filters, pagination)","entity creation/update payloads","lineage analysis parameters"],"output_types":["typed entity objects (Table, Column, Dashboard, etc.)","lineage graphs","access control policies"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-open-metadata-openmetadata__cap_11","uri":"capability://automation.workflow.kubernetes.operator.for.automated.deployment.and.lifecycle.management","name":"kubernetes operator for automated deployment and lifecycle management","description":"OpenMetadata provides a Kubernetes operator that automates deployment, scaling, and lifecycle management of OpenMetadata components (backend service, ingestion scheduler, search cluster) on Kubernetes. The operator manages configuration, database migrations, and service dependencies, enabling declarative infrastructure-as-code deployment with automatic reconciliation.","intents":["I want to deploy OpenMetadata to our Kubernetes cluster with a single Helm chart","I need to scale the OpenMetadata backend service based on API load","I want to automate database migrations and configuration updates without manual intervention"],"best_for":["organizations running Kubernetes in production","teams practicing GitOps and infrastructure-as-code","organizations needing automated scaling and high availability"],"limitations":["Operator requires Kubernetes 1.16+; not suitable for non-Kubernetes deployments","Custom resource definitions (CRDs) must be installed before deploying OpenMetadata","Operator does not manage external dependencies (Elasticsearch, database) — requires separate operators or manual setup","Debugging operator issues requires Kubernetes knowledge and access to cluster logs"],"requires":["Kubernetes 1.16+ cluster","Helm 3.0+ for chart installation","Persistent storage for database and Elasticsearch","Container registry access for OpenMetadata images"],"input_types":["Kubernetes CRD manifests","Helm values for configuration"],"output_types":["deployed OpenMetadata pods and services","persistent volumes for data storage","service endpoints for API access"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-open-metadata-openmetadata__cap_12","uri":"capability://data.processing.analysis.bulk.metadata.import.export.with.csv.and.json.support","name":"bulk metadata import/export with csv and json support","description":"OpenMetadata supports bulk import and export of metadata entities (tables, columns, glossary terms, owners) via CSV and JSON formats, enabling migration from other metadata platforms, backup/restore workflows, and integration with external metadata sources. The import process validates schemas, handles duplicates, and provides detailed error reports for failed records.","intents":["I want to migrate metadata from our legacy data catalog to OpenMetadata using a CSV export","I need to bulk update ownership and descriptions for 5000 tables from a spreadsheet","I want to back up all metadata to JSON files for disaster recovery"],"best_for":["organizations migrating from other metadata platforms","teams managing metadata in spreadsheets or external systems","organizations with large metadata catalogs requiring bulk operations"],"limitations":["CSV import does not support complex relationships (lineage, contracts); only basic entity properties","Import validation is basic; complex business rules must be enforced post-import","Large imports (10000+ rows) may timeout; requires pagination or chunking","Export does not include access control policies or audit logs"],"requires":["CSV or JSON file with entity definitions","OpenMetadata backend with API access","Proper formatting of input files matching OpenMetadata schema"],"input_types":["CSV files with table/column/glossary definitions","JSON files with entity payloads"],"output_types":["imported entities in OpenMetadata","error reports with validation failures","exported metadata in CSV/JSON format"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-open-metadata-openmetadata__cap_13","uri":"capability://data.processing.analysis.data.profiler.with.statistical.analysis.and.distribution.tracking","name":"data profiler with statistical analysis and distribution tracking","description":"OpenMetadata's data profiler analyzes table and column statistics (row count, null percentage, cardinality, min/max, distribution histograms) on a schedule and stores historical trends. The profiler integrates with the ingestion framework to run after data loads, enabling detection of data quality anomalies through statistical comparison with historical baselines.","intents":["I want to understand the distribution of values in a column and detect when it changes significantly","I need to track how many null values appear in each column over time to detect data quality issues","I want to identify columns with low cardinality that might be good candidates for partitioning"],"best_for":["data teams monitoring data quality through statistical analysis","organizations implementing data observability with profiling","teams optimizing database performance through cardinality analysis"],"limitations":["Profiling requires direct database access and can be resource-intensive on large tables","Statistical analysis is limited to basic metrics; no advanced anomaly detection or ML-based outlier detection","Profiling results are stored in OpenMetadata; no integration with external analytics tools","Scheduling depends on Airflow; profiling cannot be triggered on-demand from the UI"],"requires":["Database credentials with SELECT permissions","Apache Airflow for scheduling profiling jobs","Sufficient database resources to run profiling queries"],"input_types":["table/column selection","profiling schedule configuration"],"output_types":["statistical metrics (null count, cardinality, distribution)","historical trend data","profiling reports and visualizations"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-open-metadata-openmetadata__cap_2","uri":"capability://data.processing.analysis.data.quality.profiling.and.automated.test.execution","name":"data quality profiling and automated test execution","description":"OpenMetadata profiles table and column statistics (null counts, cardinality, distribution, data types) and executes parameterized data quality tests (null checks, uniqueness, range validation, custom SQL assertions) on a schedule. Test results are stored with historical trends, enabling detection of data quality regressions and integration with data observability workflows through event-driven notifications.","intents":["I want to automatically profile my tables daily and track changes in data distribution over time","I need to set up quality checks that alert me when null rates exceed thresholds or duplicate counts spike","I want to validate that a data pipeline's output meets expected schema and value constraints before it's used downstream"],"best_for":["data engineering teams implementing data quality gates in pipelines","data observability teams monitoring data health across warehouses","organizations with SLAs on data freshness and quality"],"limitations":["Profiling and test execution require direct database access with appropriate query permissions","Test definitions are UI-based or JSON; no native support for complex statistical tests or ML-based anomaly detection","Scheduling depends on Airflow; tests cannot be triggered in real-time from data ingestion events","Historical trend analysis is limited to metrics stored in OpenMetadata; requires external tools for advanced statistical analysis"],"requires":["Database credentials with SELECT permissions on target tables","Apache Airflow for scheduling profiling and test jobs","OpenMetadata backend with sufficient storage for historical metrics"],"input_types":["table/column selection from metadata catalog","test configuration (thresholds, assertions, schedules)","custom SQL for advanced assertions"],"output_types":["profiling statistics (null count, cardinality, min/max, distribution)","test execution results (pass/fail, timestamp, metric values)","quality trend reports and alerts"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-open-metadata-openmetadata__cap_3","uri":"capability://memory.knowledge.semantic.metadata.and.data.contracts.management","name":"semantic metadata and data contracts management","description":"OpenMetadata enables teams to define data contracts (schema, quality SLAs, ownership, update frequency) as versioned metadata entities, attach semantic annotations (business glossary terms, tags, descriptions) to tables and columns, and enforce contract compliance through automated validation. Contracts are queryable and can be integrated into CI/CD pipelines to prevent breaking changes to data assets.","intents":["I want to define a contract for my customer_id column specifying it must be unique, non-null, and updated daily","I need to tag all PII columns with a 'sensitive' tag and track which teams have access to them","I want to prevent downstream teams from modifying a table schema without approval from the data owner"],"best_for":["data governance teams enforcing data standards and ownership","organizations implementing data mesh with decentralized ownership","teams integrating data quality into CI/CD pipelines"],"limitations":["Contract enforcement is advisory (via UI warnings and API responses); no native database-level constraints are created","Versioning and change tracking are metadata-level only; does not prevent actual schema changes at the source","Integration with CI/CD requires custom scripts or webhooks; no native GitHub/GitLab actions provided","Glossary and semantic annotations are managed in OpenMetadata UI; no bulk import from external business glossaries"],"requires":["OpenMetadata backend with API access","User roles and permissions configured for contract ownership","Optional: CI/CD pipeline integration via webhooks or API calls"],"input_types":["contract definitions (schema, SLAs, ownership)","glossary terms and semantic tags","change notifications from source systems"],"output_types":["versioned contract entities","compliance reports and violation alerts","API responses for contract validation"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-open-metadata-openmetadata__cap_4","uri":"capability://search.retrieval.semantic.search.and.discovery.with.vector.embeddings","name":"semantic search and discovery with vector embeddings","description":"OpenMetadata indexes metadata entities (tables, columns, dashboards, glossary terms) using Elasticsearch or OpenSearch with full-text search, and optionally generates vector embeddings of descriptions and metadata to enable semantic similarity search. Users can search by natural language queries (e.g., 'customer revenue metrics') and receive ranked results based on relevance, with faceted filtering by owner, domain, and data type.","intents":["I want to search for 'customer lifetime value' and find all tables and dashboards related to customer metrics","I need to discover tables owned by a specific team that contain PII data","I want to find similar columns across my data warehouse to identify duplicate definitions"],"best_for":["data analysts discovering datasets without knowing exact table names","data governance teams finding all assets related to a business concept","organizations with large metadata catalogs (1000+ tables)"],"limitations":["Search quality depends on metadata quality — sparse or missing descriptions reduce relevance","Vector embeddings require additional compute and storage; not enabled by default","Elasticsearch/OpenSearch cluster must be maintained separately; adds operational complexity","Search results are metadata-only; does not preview actual data or sample rows"],"requires":["Elasticsearch 7.0+ or OpenSearch 1.0+ cluster","Metadata ingestion completed for entities to be searchable","Optional: LLM API key for semantic embedding generation"],"input_types":["natural language search queries","filter criteria (owner, domain, data type, tags)"],"output_types":["ranked list of metadata entities with relevance scores","faceted search results","entity detail pages with lineage and contracts"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-open-metadata-openmetadata__cap_5","uri":"capability://safety.moderation.role.based.access.control.and.data.lineage.aware.permissions","name":"role-based access control and data lineage-aware permissions","description":"OpenMetadata enforces role-based access control (RBAC) at the entity level (table, column, dashboard) with support for custom roles and permissions. Access policies can be defined based on data lineage — for example, granting read access to all downstream tables when a user has access to an upstream source — enabling permission inheritance through the data pipeline.","intents":["I want to restrict access to PII columns to only the data privacy team","I need to grant a team access to a dashboard and automatically grant them access to all upstream tables in the lineage","I want to audit which users accessed which data assets and when"],"best_for":["organizations with strict data governance and compliance requirements","teams implementing data mesh with decentralized access control","regulated industries (finance, healthcare) requiring audit trails"],"limitations":["RBAC is metadata-level only; does not enforce access at the database level — requires integration with source system access controls","Lineage-based permission inheritance requires lineage to be fully mapped; incomplete lineage results in incomplete permission propagation","Audit logs are stored in OpenMetadata; no native integration with external SIEM systems","Custom role definitions require API calls; no UI for role creation"],"requires":["OpenMetadata backend with authentication configured (LDAP, OAuth, SAML)","User and team definitions in OpenMetadata","Optional: integration with source system access controls via connectors"],"input_types":["role definitions and permissions","user/team assignments","lineage relationships for permission inheritance"],"output_types":["access control policies","audit logs with user actions and timestamps","permission inheritance reports"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-open-metadata-openmetadata__cap_6","uri":"capability://automation.workflow.team.collaboration.and.asset.ownership.tracking","name":"team collaboration and asset ownership tracking","description":"OpenMetadata enables teams to claim ownership of data assets (tables, dashboards, domains), add descriptions and documentation, and collaborate through comments and activity feeds. Ownership is tracked at the entity level with support for multiple owners, and changes to assets trigger notifications to owners and stakeholders, creating accountability and enabling self-service metadata management.","intents":["I want to assign ownership of a table to a specific team and notify them of any schema changes","I need to add documentation and examples to a table so other teams understand how to use it","I want to see all changes made to a dataset and who made them through an activity feed"],"best_for":["organizations with decentralized data ownership (data mesh)","teams using OpenMetadata as a collaborative data documentation platform","organizations wanting to reduce metadata maintenance burden through crowdsourcing"],"limitations":["Ownership is advisory; does not enforce access control or prevent unauthorized changes","Notifications are in-app only; no native email or Slack integration (requires custom webhooks)","Activity feed is metadata-only; does not track actual data changes at the source","Bulk ownership assignment requires API calls; no CSV import for ownership"],"requires":["OpenMetadata backend with user/team management configured","Optional: webhook integration for external notifications"],"input_types":["ownership assignments (user or team)","descriptions and documentation","comments and feedback"],"output_types":["entity ownership records","activity feed with timestamps and user attribution","notification events"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-open-metadata-openmetadata__cap_7","uri":"capability://tool.use.integration.mcp.server.integration.for.llm.powered.metadata.queries","name":"mcp server integration for llm-powered metadata queries","description":"OpenMetadata exposes a Model Context Protocol (MCP) server that allows LLMs and AI agents to query metadata, execute lineage analysis, and retrieve data contracts through a standardized interface. The MCP server handles authentication, context enrichment, and response formatting, enabling natural language queries like 'show me all tables owned by the finance team with PII data' to be executed against the metadata catalog.","intents":["I want to ask an AI agent 'which tables contain customer PII and who owns them' and get a structured response","I need to integrate OpenMetadata metadata into my LLM-powered data discovery chatbot","I want to use Claude or GPT to analyze data lineage and suggest data quality improvements"],"best_for":["teams building LLM-powered data discovery and governance tools","organizations integrating OpenMetadata with AI agents for metadata analysis","developers building natural language interfaces to metadata catalogs"],"limitations":["MCP server requires OpenMetadata backend to be running and accessible","LLM responses are only as good as the metadata quality in OpenMetadata","No built-in rate limiting or quota management for MCP requests","Requires understanding of MCP protocol and OpenMetadata API schema"],"requires":["OpenMetadata backend running with API access","MCP client implementation (e.g., in Claude, GPT, or custom agent)","Authentication credentials for OpenMetadata API"],"input_types":["natural language queries","MCP tool calls with parameters"],"output_types":["structured metadata responses","lineage analysis results","contract compliance reports"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-open-metadata-openmetadata__cap_8","uri":"capability://memory.knowledge.domain.and.glossary.management.with.semantic.relationships","name":"domain and glossary management with semantic relationships","description":"OpenMetadata provides a hierarchical domain structure for organizing data assets by business area, and a glossary system for defining business terms with relationships (synonyms, parent/child, related terms). Glossary terms can be linked to table and column metadata, enabling semantic understanding of data and supporting data governance through standardized business vocabulary.","intents":["I want to organize my data assets into domains (Finance, Marketing, Operations) and assign ownership at the domain level","I need to define a business glossary with terms like 'Customer Lifetime Value' and link them to the columns that calculate them","I want to ensure consistent naming across my data warehouse by defining approved terms and their relationships"],"best_for":["organizations implementing data governance with business glossaries","teams organizing large metadata catalogs by business domain","regulated industries requiring standardized business terminology"],"limitations":["Glossary is metadata-only; does not enforce naming conventions at the database level","Bulk glossary import requires CSV format; no native integration with external glossary tools","Semantic relationships are manually defined; no automatic synonym detection","Domain hierarchy is flat in the UI; no multi-level nesting"],"requires":["OpenMetadata backend with glossary module enabled","CSV file with glossary terms (for bulk import)"],"input_types":["glossary term definitions","semantic relationships (synonyms, parent/child)","term-to-column mappings"],"output_types":["hierarchical domain structure","glossary with relationships","term-to-asset mappings"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-open-metadata-openmetadata__cap_9","uri":"capability://automation.workflow.event.driven.metadata.updates.and.webhook.notifications","name":"event-driven metadata updates and webhook notifications","description":"OpenMetadata publishes events (entity created, updated, deleted, lineage changed, quality test failed) to an event bus (Kafka, webhook) that external systems can subscribe to. This enables real-time metadata synchronization with downstream tools, triggering workflows when data assets change, and maintaining eventual consistency across the data stack without polling.","intents":["I want to trigger a data quality check whenever a table schema changes","I need to update my BI tool's metadata cache whenever OpenMetadata detects a lineage change","I want to send a Slack notification to a team whenever they're assigned ownership of a new data asset"],"best_for":["organizations with event-driven data architectures","teams integrating OpenMetadata with multiple downstream tools","organizations needing real-time metadata synchronization"],"limitations":["Event delivery is at-least-once; requires idempotent handling of duplicate events","Webhook retries are limited; failed deliveries require manual intervention","Event schema is fixed; no custom event types or payloads","Kafka integration requires separate Kafka cluster; adds operational complexity"],"requires":["OpenMetadata backend with event system configured","Kafka cluster (optional) or webhook endpoint for event consumption","Consumer implementation to handle event payloads"],"input_types":["metadata change events","quality test results","lineage updates"],"output_types":["event stream (Kafka topic or webhook POST)","event payloads with entity details and change information"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+ for ingestion framework","Source system credentials and network access","Apache Airflow 2.0+ for scheduled ingestion (optional but recommended)","OpenMetadata backend service running with API access","dbt manifest files or Airflow DAG definitions for transformation logic","SQL parser support for source dialect (Snowflake, BigQuery, PostgreSQL, etc.)","Metadata for upstream and downstream systems already ingested into OpenMetadata","Java 11+","Maven or Gradle for dependency management","OpenMetadata backend with API access and authentication configured"],"failure_modes":["Connector coverage varies by source — some sources have basic extraction only, others support full lineage","Incremental ingestion requires source-specific change tracking capabilities; not all sources support efficient delta extraction","Scheduling depends on Airflow availability — requires separate Airflow deployment for production scheduling","Custom connector development requires understanding OpenMetadata's Python SDK and entity model","Lineage extraction accuracy depends on SQL parser capabilities — complex CTEs, dynamic SQL, and procedural logic may not be fully resolved","Requires explicit lineage metadata from sources (dbt manifest, Airflow task dependencies); implicit lineage from unstructured code is not extracted","Cross-system lineage (e.g., Kafka → Spark → Snowflake) requires connectors for each system to be configured","Real-time lineage updates depend on source system's change notification capabilities","Java SDK only; no native Python, Go, or Node.js SDKs (Python SDK exists but is separate)","SDK version must match OpenMetadata backend version; breaking changes between versions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7073396536075868,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:31.492Z","last_commit":"2026-05-03T10:12:51Z"},"community":{"stars":13781,"forks":2079,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-open-metadata-openmetadata","compare_url":"https://unfragile.ai/compare?artifact=mcp-open-metadata-openmetadata"}},"signature":"F5ZIDNdqIv5Kp+sAtZdUGaXZDviTb5fLtGk27H589/09lQ/IW9cYcnqjMxIoSkA/NUHUl8Yx/ng1Fr5CPB1DDQ==","signedAt":"2026-06-23T00:58:16.228Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-open-metadata-openmetadata","artifact":"https://unfragile.ai/mcp-open-metadata-openmetadata","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-open-metadata-openmetadata","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"}}