{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-atlan","slug":"atlan","name":"Atlan","type":"mcp","url":"https://github.com/atlanhq/agent-toolkit/tree/main/modelcontextprotocol","page_url":"https://unfragile.ai/atlan","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-atlan__cap_0","uri":"capability://tool.use.integration.metadata.aware.context.injection.for.llm.agents","name":"metadata-aware context injection for llm agents","description":"Implements MCP server protocol to expose Atlan's metadata catalog as a standardized tool interface that LLM agents can query and reference. Uses the Model Context Protocol to establish bidirectional communication between AI tools (Claude, etc.) and Atlan's metadata platform, allowing agents to retrieve business context, data lineage, and asset relationships without custom API integration code. Metadata is injected into the agent's context window as structured tool definitions that the LLM can invoke during reasoning.","intents":["I want my AI agent to understand data lineage and relationships when answering questions about our data assets","I need to give Claude or other LLMs access to our metadata catalog without building custom API wrappers","I want agents to reference business glossaries and data governance policies during decision-making","I need to enrich LLM prompts with real-time metadata context from our data platform"],"best_for":["data teams building AI agents that need to understand data relationships and lineage","enterprises using Claude or other MCP-compatible LLMs who want metadata-aware AI","organizations with existing Atlan deployments looking to add AI capabilities","builders creating data governance or data discovery agents"],"limitations":["Requires Atlan instance with API access — cannot work standalone","MCP protocol support limited to compatible clients (Claude Desktop, some IDEs); not all LLM platforms support MCP yet","Metadata freshness depends on Atlan's sync frequency — real-time changes may have latency","Context window constraints mean large metadata graphs may need filtering/pagination","No built-in caching layer — repeated queries hit Atlan API each time"],"requires":["Atlan instance (cloud or self-hosted) with API credentials","MCP-compatible client (Claude Desktop, VS Code with MCP extension, or custom MCP client)","Network connectivity from MCP client to Atlan API endpoint","Python 3.8+ (if running MCP server locally)"],"input_types":["natural language queries from LLM","structured metadata queries (asset names, types, lineage paths)","filter parameters (business domain, owner, classification)"],"output_types":["structured metadata objects (assets, lineage, relationships)","formatted text descriptions of data assets","JSON-serialized metadata for agent reasoning"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-atlan__cap_1","uri":"capability://tool.use.integration.data.lineage.traversal.for.agent.reasoning","name":"data lineage traversal for agent reasoning","description":"Exposes Atlan's lineage graph as queryable tools that agents can use to understand upstream/downstream data dependencies and transformation chains. The MCP server translates lineage queries into Atlan API calls, returning structured parent-child relationships, transformation logic, and impact analysis. Agents can traverse lineage paths to answer questions like 'what data feeds into this dashboard' or 'what will break if we change this table schema'.","intents":["I want an agent to explain the data lineage for a specific table or dashboard to business users","I need agents to perform impact analysis — show me all downstream assets affected by a schema change","I want agents to trace data quality issues back to their source by walking the lineage graph","I need to audit data flow compliance by having agents traverse lineage paths and check governance policies"],"best_for":["data engineers building AI-powered data discovery tools","data governance teams automating impact analysis and compliance checks","analytics teams creating self-service data lineage explanations for non-technical users","organizations with complex multi-hop data pipelines needing AI-assisted navigation"],"limitations":["Lineage accuracy depends on Atlan's ingestion connectors — gaps in connector coverage mean incomplete lineage graphs","Deep lineage traversal (10+ hops) may hit performance limits or context window constraints","Circular dependencies or complex fan-out patterns may confuse agent reasoning without explicit handling","Lineage is static snapshots — does not reflect real-time data flow or runtime dependencies","Requires lineage to be pre-ingested into Atlan; cannot infer lineage from raw code or logs"],"requires":["Atlan instance with lineage data already ingested from source systems","Connectors configured for the data platforms you want to query (Snowflake, Databricks, dbt, etc.)","API credentials with read access to lineage entities","MCP client with tool-calling capability"],"input_types":["asset identifiers (qualified names, GUIDs)","lineage direction (upstream, downstream, bidirectional)","depth parameters (how many hops to traverse)","filter criteria (asset types, owners, classifications)"],"output_types":["lineage graphs as JSON (nodes and edges)","flattened lineage paths (list of assets in order)","impact analysis reports (affected assets, risk levels)","natural language summaries of lineage chains"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-atlan__cap_2","uri":"capability://memory.knowledge.business.metadata.and.glossary.lookup.for.context.enrichment","name":"business metadata and glossary lookup for context enrichment","description":"Provides MCP tools to query Atlan's business glossary, classifications, and custom metadata attributes, allowing agents to understand business context and governance policies. Agents can look up term definitions, ownership information, data quality rules, and classification tags to enrich their reasoning. The server translates glossary queries into Atlan API calls and returns structured metadata that agents can incorporate into responses or decision-making logic.","intents":["I want agents to explain what a data asset means in business terms by looking up glossary definitions","I need agents to check data governance policies and classifications before recommending data access","I want agents to identify data owners and stewards when users ask 'who manages this dataset'","I need agents to enforce data quality rules by checking classifications and metadata attributes"],"best_for":["organizations with mature data governance and business glossaries in Atlan","teams building self-service data discovery interfaces for non-technical users","data stewardship teams automating governance checks and policy enforcement","enterprises needing AI-powered data documentation and context"],"limitations":["Glossary quality depends on manual curation — incomplete or outdated definitions reduce agent accuracy","Custom metadata attributes must be pre-defined in Atlan; agents cannot dynamically create new attributes","No full-text search across glossary definitions — queries must match exact terms or use predefined filters","Glossary updates have latency — agents may reference stale definitions until cache refreshes","Classification hierarchies can be complex; agents may struggle with multi-level classification reasoning"],"requires":["Atlan instance with business glossary populated","Custom metadata attributes defined in Atlan's metadata model","Classifications and tags configured for your data assets","API credentials with read access to business metadata"],"input_types":["glossary term names or IDs","asset qualified names (to retrieve associated metadata)","classification names or tags","custom attribute keys"],"output_types":["glossary term definitions and descriptions","ownership and stewardship information","classification hierarchies and tag lists","custom metadata attribute values","formatted business context summaries"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-atlan__cap_3","uri":"capability://search.retrieval.asset.search.and.discovery.via.semantic.and.structured.queries","name":"asset search and discovery via semantic and structured queries","description":"Implements MCP tools for searching Atlan's asset catalog using both structured filters (asset type, owner, classification) and semantic search capabilities. The server translates search queries into Atlan API calls, supporting full-text search, faceted filtering, and potentially semantic embeddings if Atlan has indexed assets. Agents can discover relevant data assets, users, and metadata without knowing exact asset names, enabling exploratory data discovery workflows.","intents":["I want agents to find relevant datasets when users ask 'show me all customer data' without knowing exact table names","I need agents to search for assets by business domain, owner, or quality level","I want agents to discover related assets by semantic similarity (e.g., 'find tables similar to this one')","I need agents to search across asset types (tables, dashboards, reports, models) in a single query"],"best_for":["data discovery and self-service analytics teams","organizations with large asset catalogs (1000+ assets) where browsing is impractical","teams building AI-powered data search interfaces","enterprises needing semantic search across heterogeneous data assets"],"limitations":["Search quality depends on asset metadata completeness — poorly documented assets may not surface in results","Semantic search (if available) requires pre-computed embeddings; not all Atlan instances may have this enabled","Search results are paginated; agents may need multiple queries to explore large result sets","Faceted filtering limited to pre-defined facets in Atlan; custom facets require schema changes","Search latency may be high for complex queries across large catalogs (100k+ assets)"],"requires":["Atlan instance with asset catalog populated","Assets indexed with metadata (descriptions, tags, classifications)","API credentials with read access to search endpoints","Optional: semantic embeddings enabled in Atlan for similarity search"],"input_types":["free-text search queries","structured filters (asset type, owner, classification, domain)","pagination parameters (limit, offset)","sort criteria (relevance, recency, popularity)"],"output_types":["asset search results (name, type, owner, description)","faceted counts (how many assets match each filter)","relevance scores and ranking","asset metadata summaries","formatted search result cards for display"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-atlan__cap_4","uri":"capability://memory.knowledge.asset.metadata.retrieval.and.enrichment.for.agent.context","name":"asset metadata retrieval and enrichment for agent context","description":"Provides MCP tools to fetch detailed metadata for specific assets (tables, dashboards, models, etc.) from Atlan, including schema information, ownership, quality metrics, and custom attributes. Agents can retrieve comprehensive asset profiles to answer detailed questions or validate assumptions before recommending data usage. The server handles asset lookups by qualified name, GUID, or search results, returning structured metadata that agents can parse and reason about.","intents":["I want agents to fetch full asset details (schema, owner, quality score) when users ask about a specific table","I need agents to check data quality metrics before recommending a dataset for analysis","I want agents to retrieve schema information to help users understand table structure and column meanings","I need agents to validate asset ownership and access policies before suggesting data usage"],"best_for":["data teams building AI-powered data documentation and exploration tools","organizations needing agents to validate data quality before recommendations","teams automating data access requests with AI-powered asset validation","enterprises building AI-powered data catalogs with rich asset profiles"],"limitations":["Metadata retrieval is synchronous — large asset profiles may add latency to agent responses","Schema information may be incomplete if source system connectors don't fully extract column-level metadata","Custom attributes must be pre-defined in Atlan; agents cannot dynamically add new attributes","Quality metrics are only available if Atlan has integrations with quality monitoring tools","Asset relationships (lineage, dependencies) require separate queries; not included in basic metadata retrieval"],"requires":["Atlan instance with assets and metadata populated","Asset identifiers (qualified names or GUIDs) or prior search results","API credentials with read access to asset metadata endpoints","Optional: quality monitoring integrations for quality metrics"],"input_types":["asset qualified names (e.g., 'database.schema.table')","asset GUIDs","asset types (to filter results)","metadata attributes to retrieve (optional filtering)"],"output_types":["asset metadata objects (name, type, owner, description, created date)","schema information (columns, data types, descriptions)","quality metrics (data freshness, completeness, accuracy scores)","custom metadata attributes and values","ownership and stewardship information","formatted asset profile summaries"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-atlan__cap_5","uri":"capability://tool.use.integration.multi.provider.mcp.server.deployment.and.client.compatibility","name":"multi-provider mcp server deployment and client compatibility","description":"Implements the Model Context Protocol (MCP) server specification, enabling the Atlan toolkit to work with any MCP-compatible client including Claude Desktop, VS Code extensions, and custom MCP clients. The server handles MCP protocol negotiation, tool schema definition, and request/response serialization. Supports both stdio and HTTP transport mechanisms for flexible deployment options (local, containerized, or cloud-hosted).","intents":["I want to use Atlan metadata with Claude Desktop without building custom integrations","I need to deploy the Atlan MCP server in a containerized environment for my team","I want to build a custom application that queries Atlan metadata via MCP protocol","I need to support multiple LLM clients (Claude, custom agents) with a single Atlan integration"],"best_for":["teams using Claude Desktop or other MCP-compatible LLM clients","organizations deploying Atlan integrations in containerized or cloud environments","builders creating custom MCP clients that need Atlan metadata access","enterprises standardizing on MCP for LLM tool integration"],"limitations":["MCP protocol support limited to compatible clients — older LLM platforms or custom integrations may not support MCP","Stdio transport requires local process execution — not suitable for serverless or highly distributed deployments","HTTP transport adds network latency and requires authentication/authorization management","Tool schema complexity may exceed context window limits for very large metadata catalogs","No built-in rate limiting or request queuing — high-volume agent usage may overwhelm Atlan API"],"requires":["MCP-compatible client (Claude Desktop, VS Code, or custom MCP client)","Python 3.8+ (if running MCP server locally)","Docker (if containerizing the MCP server)","Network connectivity from MCP client to Atlan API","Atlan API credentials (API key or OAuth token)"],"input_types":["MCP protocol requests (tool calls with arguments)","transport mechanism (stdio or HTTP)"],"output_types":["MCP protocol responses (tool results, errors)","structured metadata from Atlan APIs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-atlan__cap_6","uri":"capability://safety.moderation.access.control.and.permission.validation.for.agent.operations","name":"access control and permission validation for agent operations","description":"Implements MCP-level access control that validates agent operations against Atlan's role-based access control (RBAC) and attribute-based access control (ABAC) policies. Enforces permissions at the tool invocation level, ensuring agents can only access metadata and perform operations that their API credentials permit, with detailed permission validation results that agents can use to handle access denied scenarios gracefully.","intents":["I want to ensure my agent respects data access policies and cannot expose restricted metadata to unauthorized users","I need to validate that an agent operation is permitted before executing it, with clear error messages for denied operations","I want to audit all agent operations and ensure they comply with access control policies"],"best_for":["enterprises deploying agents in regulated environments requiring strict access control","multi-tenant platforms where agents need to respect per-user access policies","organizations with complex data governance requiring fine-grained permission validation"],"limitations":["Access control is enforced at API credential level; agents inherit permissions of their credentials, limiting per-user delegation","Permission validation adds latency to each agent operation; high-frequency operations may experience performance impact","ABAC policies are evaluated synchronously; complex attribute-based rules may slow agent execution","No built-in support for dynamic permission delegation; agents cannot request elevated permissions for specific operations"],"requires":["Atlan instance with RBAC/ABAC policies configured","API credentials with appropriate permissions for agent operations","Defined roles and attribute-based policies in Atlan"],"input_types":["agent operation context (asset IDs, operation type, metadata fields)","user/agent identity and credentials","permission scope parameters"],"output_types":["permission validation results (allowed/denied)","detailed permission denial reasons","audit log entries for access control events","available operations for current credentials"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Atlan instance (cloud or self-hosted) with API credentials","MCP-compatible client (Claude Desktop, VS Code with MCP extension, or custom MCP client)","Network connectivity from MCP client to Atlan API endpoint","Python 3.8+ (if running MCP server locally)","Atlan instance with lineage data already ingested from source systems","Connectors configured for the data platforms you want to query (Snowflake, Databricks, dbt, etc.)","API credentials with read access to lineage entities","MCP client with tool-calling capability","Atlan instance with business glossary populated","Custom metadata attributes defined in Atlan's metadata model"],"failure_modes":["Requires Atlan instance with API access — cannot work standalone","MCP protocol support limited to compatible clients (Claude Desktop, some IDEs); not all LLM platforms support MCP yet","Metadata freshness depends on Atlan's sync frequency — real-time changes may have latency","Context window constraints mean large metadata graphs may need filtering/pagination","No built-in caching layer — repeated queries hit Atlan API each time","Lineage accuracy depends on Atlan's ingestion connectors — gaps in connector coverage mean incomplete lineage graphs","Deep lineage traversal (10+ hops) may hit performance limits or context window constraints","Circular dependencies or complex fan-out patterns may confuse agent reasoning without explicit handling","Lineage is static snapshots — does not reflect real-time data flow or runtime dependencies","Requires lineage to be pre-ingested into Atlan; cannot infer lineage from raw code or logs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.39,"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":"2026-06-17T09:51:02.371Z","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=atlan","compare_url":"https://unfragile.ai/compare?artifact=atlan"}},"signature":"nbRCNe0pB9EavmHSjdyMNLFUoS42bHgOd5CtsrnEqZPkKTfG3jPs5YwnZSh0B7iJP4eVxNoucmVjM9Jby/vpAw==","signedAt":"2026-06-21T18:17:53.698Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/atlan","artifact":"https://unfragile.ai/atlan","verify":"https://unfragile.ai/api/v1/verify?slug=atlan","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"}}