{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-cyanheads-atlas-mcp-server","slug":"mcp-cyanheads-atlas-mcp-server","name":"atlas-mcp-server","type":"mcp","url":"https://github.com/cyanheads/atlas-mcp-server","page_url":"https://unfragile.ai/mcp-cyanheads-atlas-mcp-server","categories":["mcp-servers"],"tags":["agents","ai","deep-research","graph-database","llm","llm-agent","mcp","modelcontextprotocol","neo4j","projects-tasks-knowledge","task-management"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-cyanheads-atlas-mcp-server__cap_0","uri":"capability://data.processing.analysis.hierarchical.project.task.knowledge.graph.modeling.via.neo4j","name":"hierarchical project-task-knowledge graph modeling via neo4j","description":"Implements a three-tier data model where Projects contain Tasks and Knowledge entities as distinct node types in Neo4j, with relationship edges defining containment and dependency chains. Uses Cypher query language for traversal and aggregation across the hierarchy, enabling agents to structure complex workflows with nested task dependencies and associated knowledge artifacts without flattening the organizational structure.","intents":["I need to organize multi-level project hierarchies where tasks depend on knowledge artifacts","I want to query task dependencies and knowledge relationships across a project tree","I need to maintain structured metadata about projects, tasks, and knowledge without losing relational context"],"best_for":["LLM agents managing complex, multi-stage workflows with knowledge dependencies","teams building AI-driven project management systems with graph-based reasoning","developers needing persistent hierarchical state for long-running agent tasks"],"limitations":["Neo4j instance required — no embedded/in-memory fallback for local development","Cypher query complexity grows with deep hierarchies (5+ levels); no automatic query optimization","No built-in versioning of graph snapshots — requires external audit logging for change tracking"],"requires":["Neo4j 4.4+ instance (local or cloud)","Node.js 18+","TypeScript 4.9+","neo4j driver package installed"],"input_types":["project metadata (name, description, status)","task definitions (title, priority, assignee, dependencies)","knowledge items (content, tags, relationships)"],"output_types":["graph node/edge structures","Cypher query results (JSON)","relationship traversal paths"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-cyanheads-atlas-mcp-server__cap_1","uri":"capability://tool.use.integration.mcp.tool.based.crud.operations.for.projects.tasks.and.knowledge","name":"mcp tool-based crud operations for projects, tasks, and knowledge","description":"Exposes project, task, and knowledge management operations as MCP tools with standardized input schemas and response formatting. Each tool (create, read, update, delete, list) maps to Neo4j service methods that validate inputs via Zod schemas, execute Cypher mutations/queries, and return structured JSON responses. Tools are discoverable by MCP clients and include detailed descriptions for LLM agent planning.","intents":["I want my LLM agent to create and manage projects without direct database access","I need standardized tool interfaces for task CRUD operations that the agent can call reliably","I want to expose project/task/knowledge operations through MCP without building custom APIs"],"best_for":["developers building LLM agents that need persistent project/task management","teams integrating ATLAS into multi-agent systems via MCP","non-technical users who want agents to manage workflows through a standard protocol"],"limitations":["Tool discovery is static at server startup — new tools require server restart","No built-in pagination for list operations; large result sets returned in full","Tool input validation happens at MCP layer; no pre-flight checks for business logic constraints (e.g., circular dependencies)"],"requires":["MCP client implementation (e.g., Claude Desktop, custom MCP client)","Atlas MCP server running and accessible","Neo4j database configured and connected"],"input_types":["JSON tool arguments matching Zod schemas","project/task/knowledge identifiers (UUIDs)","metadata objects (name, description, status, priority)"],"output_types":["JSON responses with created/updated entity data","list results with entity arrays","success/error status messages"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-cyanheads-atlas-mcp-server__cap_10","uri":"capability://safety.moderation.error.handling.and.structured.logging.across.all.layers","name":"error handling and structured logging across all layers","description":"Implements consistent error handling with typed error classes (ValidationError, NotFoundError, DatabaseError, etc.) and structured logging using Winston or Pino. All errors include context (request ID, operation type, entity ID) and are logged with appropriate severity levels. HTTP responses include error codes and messages; MCP responses include error details in the response object.","intents":["I want clear error messages when operations fail so I can debug agent issues","I need to log all errors with context for monitoring and alerting","I want consistent error handling across tools, resources, and database operations"],"best_for":["operators monitoring ATLAS deployments for errors and anomalies","developers debugging agent failures and unexpected behavior","teams implementing error tracking and alerting (Sentry, DataDog)"],"limitations":["Error messages are logged but not automatically sent to external services; requires integration","Sensitive data (passwords, tokens) must be manually filtered from logs","Log volume can be high with verbose logging; no built-in sampling or filtering","Error recovery is not automatic; agents must handle errors and retry"],"requires":["logging library (Winston, Pino) configured","optional: error tracking service (Sentry, DataDog) for alerting"],"input_types":["automatic: errors thrown by operations"],"output_types":["structured log entries with context metadata","error responses with codes and messages","optional: error events sent to external services"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-cyanheads-atlas-mcp-server__cap_11","uri":"capability://automation.workflow.configuration.management.with.environment.variable.validation","name":"configuration management with environment variable validation","description":"Uses Zod to validate and parse environment variables at startup, ensuring all required configuration is present and correctly typed before the server starts. Supports configuration for database connection, server ports, authentication secrets, logging levels, and feature flags. Provides clear error messages if configuration is invalid or missing.","intents":["I want to configure ATLAS via environment variables without editing code","I need validation to ensure all required configuration is present before startup","I want different configurations for dev, staging, and production environments"],"best_for":["DevOps engineers deploying ATLAS to multiple environments","developers setting up local development instances","teams using containerized deployments (Docker, Kubernetes)"],"limitations":["Configuration is read-only at startup; changes require server restart","No built-in support for configuration hot-reloading","Secrets (API keys, database passwords) are stored in environment variables; no secret management integration","No configuration file support; all configuration must be environment variables"],"requires":["environment variables set before server startup","Zod package for validation"],"input_types":["environment variables (strings)"],"output_types":["validated configuration object with typed properties","error messages if validation fails"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-cyanheads-atlas-mcp-server__cap_2","uri":"capability://search.retrieval.unified.full.text.and.semantic.search.across.projects.tasks.and.knowledge","name":"unified full-text and semantic search across projects, tasks, and knowledge","description":"Provides a single search interface that queries across all three entity types (Projects, Tasks, Knowledge) using Neo4j full-text indexes and optional semantic search via embeddings. Accepts a search query string, executes Cypher queries against indexed properties, and returns ranked results grouped by entity type with relevance scores. Supports filtering by project, status, and other metadata.","intents":["I want to search for tasks and knowledge across all projects with a single query","I need to find related knowledge items when working on a specific task","I want semantic search to find conceptually similar items, not just keyword matches"],"best_for":["agents managing large numbers of projects and tasks who need fast discovery","teams with extensive knowledge bases requiring cross-project search","developers building search-driven agent workflows"],"limitations":["Full-text indexes must be manually created in Neo4j; no automatic index management","Semantic search requires embedding model integration (not built-in); adds latency per query","Ranking algorithm is basic (Neo4j relevance scores); no ML-based re-ranking","Search results are not paginated — large result sets returned in full"],"requires":["Neo4j full-text indexes configured on searchable properties","optional: embedding model API (OpenAI, local) for semantic search","search query string (minimum 2 characters)"],"input_types":["search query string","optional filters (project ID, entity type, status)"],"output_types":["ranked result objects with entity type, ID, title, relevance score","grouped by entity type (Projects, Tasks, Knowledge)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-cyanheads-atlas-mcp-server__cap_3","uri":"capability://planning.reasoning.deep.research.tool.with.iterative.llm.driven.investigation","name":"deep research tool with iterative llm-driven investigation","description":"Implements a research workflow where an LLM agent iteratively formulates research questions, searches the knowledge base and external sources, synthesizes findings, and refines queries based on results. The tool manages conversation history, tracks research progress, and stores findings back into the Knowledge tier. Uses chain-of-thought reasoning to decompose complex research goals into sub-questions.","intents":["I want my agent to conduct multi-step research on a topic, refining queries based on findings","I need to store research results and intermediate findings in the knowledge base","I want the agent to reason about what information is missing and search iteratively"],"best_for":["agents conducting exploratory research or due diligence tasks","teams building knowledge-intensive workflows with iterative refinement","developers needing agents to autonomously investigate topics and document findings"],"limitations":["Requires LLM with strong reasoning capabilities (GPT-4 or equivalent); weaker models produce unfocused queries","No built-in source credibility assessment — all findings treated equally","Research iterations are not bounded; agents can loop indefinitely without convergence criteria","External data sources (web search, APIs) must be manually integrated; no built-in connectors"],"requires":["LLM API access (OpenAI, Anthropic, etc.) for reasoning and query generation","optional: web search API (Google, Bing) for external research","initial research goal or question","Neo4j knowledge base to store findings"],"input_types":["research goal (text description)","optional: initial context or constraints","optional: list of allowed sources or domains"],"output_types":["research findings (structured as Knowledge entities)","research summary with sources and confidence levels","conversation history of research iterations"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-cyanheads-atlas-mcp-server__cap_4","uri":"capability://tool.use.integration.mcp.resource.based.read.only.access.to.projects.tasks.and.knowledge","name":"mcp resource-based read-only access to projects, tasks, and knowledge","description":"Exposes projects, tasks, and knowledge items as MCP resources (read-only data endpoints) that clients can subscribe to for real-time updates or fetch on-demand. Resources are formatted as text or JSON and include metadata about the entity, relationships, and child entities. Enables agents to maintain context about the current project/task state without invoking tools.","intents":["I want my agent to read the current state of a project without making changes","I need to subscribe to updates on specific tasks or knowledge items","I want to fetch related entities (child tasks, associated knowledge) in a single resource call"],"best_for":["agents that need read-only access to project state for context","MCP clients building dashboards or monitoring views","developers implementing agent memory systems that track project state"],"limitations":["Resources are read-only; no mutations through resource endpoints","Real-time subscriptions require WebSocket support; not available over stdio transport","Resource formatting is fixed (text or JSON); no custom serialization","Large entities (projects with 1000+ tasks) may exceed resource size limits"],"requires":["MCP client with resource support","entity ID (project, task, or knowledge item UUID)","optional: WebSocket connection for subscriptions"],"input_types":["resource URI (e.g., atlas://project/{id})","optional: subscription parameters"],"output_types":["JSON or text representation of entity","includes relationships and child entities","metadata (created_at, updated_at, owner)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-cyanheads-atlas-mcp-server__cap_5","uri":"capability://automation.workflow.request.context.and.correlation.tracking.for.agent.operations","name":"request context and correlation tracking for agent operations","description":"Maintains a request context (trace ID, agent ID, operation type) throughout the lifecycle of MCP operations, enabling correlation of related database mutations and tool invocations. Uses Node.js AsyncLocalStorage to propagate context without explicit parameter passing. Logs all operations with context metadata for debugging and audit trails.","intents":["I want to trace all database operations triggered by a single agent request","I need to correlate tool invocations and their side effects for debugging","I want audit logs that show which agent performed which operations"],"best_for":["teams debugging multi-step agent workflows with complex side effects","developers implementing audit logging for compliance","operators monitoring agent behavior and performance"],"limitations":["AsyncLocalStorage is Node.js-specific; no browser or cross-process context propagation","Context is lost if operations spawn detached promises without explicit propagation","Logging overhead increases with operation volume; no sampling or filtering built-in"],"requires":["Node.js 16.9+ (AsyncLocalStorage support)","logging infrastructure (Winston, Pino, or similar) for context-aware logs"],"input_types":["automatic: extracted from MCP request headers or agent metadata"],"output_types":["context object with trace ID, agent ID, operation type","correlated log entries with context metadata"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-cyanheads-atlas-mcp-server__cap_6","uri":"capability://safety.moderation.zod.based.input.validation.and.schema.enforcement.for.all.operations","name":"zod-based input validation and schema enforcement for all operations","description":"All MCP tools and database operations use Zod schemas to validate and parse inputs before execution. Schemas define required fields, types, constraints (min/max length, enum values), and transformations. Validation failures return structured error messages with field-level details. Enables type-safe operations and prevents invalid data from reaching the database.","intents":["I want to ensure agents can't create invalid projects or tasks with malformed data","I need clear error messages when tool inputs don't match expected schemas","I want to enforce business logic constraints (e.g., priority must be 1-5) at the input layer"],"best_for":["teams building robust agent systems that need strict input validation","developers who want to avoid database-level constraint violations","systems where invalid data could cause downstream agent failures"],"limitations":["Schema validation adds ~5-10ms latency per operation","Complex nested schemas can be verbose; no schema composition helpers provided","Validation errors are returned to the agent; no automatic error recovery or retry logic"],"requires":["Zod package (included in dependencies)","TypeScript for type inference from schemas"],"input_types":["JSON tool arguments","entity metadata objects"],"output_types":["validated and parsed data (type-safe)","structured validation error objects with field-level details"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-cyanheads-atlas-mcp-server__cap_7","uri":"capability://tool.use.integration.dual.transport.mcp.server.with.stdio.and.http.support","name":"dual-transport mcp server with stdio and http support","description":"Implements the MCP server specification with two transport mechanisms: stdio for local clients (IDE extensions, local agents) and HTTP for remote clients (web applications, cloud agents). Stdio transport uses JSON-RPC over stdin/stdout; HTTP transport uses REST-like endpoints with JWT authentication. Single server instance can serve both transports simultaneously.","intents":["I want to use ATLAS with my local IDE extension via stdio","I need to expose ATLAS to remote agents or web applications via HTTP","I want a single server that supports both local and remote clients"],"best_for":["developers building local IDE integrations and remote agent systems","teams with mixed deployment models (local + cloud agents)","operators who want to avoid running multiple server instances"],"limitations":["Stdio transport is synchronous and single-threaded; not suitable for high-concurrency scenarios","HTTP transport requires JWT token management; no built-in token refresh or expiration handling","Transport switching is not dynamic; clients must choose transport at connection time","Stdio transport is not suitable for long-running operations (timeouts after ~30s)"],"requires":["Node.js 18+","for HTTP: JWT secret key configured in environment","for stdio: parent process that manages stdin/stdout"],"input_types":["stdio: JSON-RPC messages over stdin","HTTP: JSON request bodies with Authorization header"],"output_types":["stdio: JSON-RPC responses over stdout","HTTP: JSON response bodies with status codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-cyanheads-atlas-mcp-server__cap_8","uri":"capability://automation.workflow.web.ui.for.visual.project.and.task.management","name":"web ui for visual project and task management","description":"Provides a browser-based interface for viewing and managing projects, tasks, and knowledge items. Displays the hierarchical structure as a tree or graph, allows drag-and-drop task organization, and provides forms for creating/editing entities. UI communicates with the ATLAS server via HTTP transport and MCP tools. Includes real-time updates via WebSocket subscriptions.","intents":["I want a visual interface to manage projects without using the CLI or agent tools","I need to see the project hierarchy and task dependencies in a graph view","I want to drag-and-drop tasks to reorganize them visually"],"best_for":["non-technical users who prefer visual interfaces over CLI/API","teams that want a dashboard for monitoring agent-managed projects","developers building hybrid systems where humans and agents collaborate"],"limitations":["UI is read-only for some operations; complex edits require CLI or API","Graph visualization can be slow with 1000+ entities; no virtualization or lazy loading","Real-time updates require WebSocket connection; falls back to polling if unavailable","UI is not mobile-responsive; designed for desktop browsers"],"requires":["modern web browser (Chrome, Firefox, Safari)","ATLAS HTTP server running and accessible","optional: WebSocket support for real-time updates"],"input_types":["user interactions (clicks, drags, form submissions)"],"output_types":["visual representations (tree, graph, list views)","form data for creating/editing entities"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-cyanheads-atlas-mcp-server__cap_9","uri":"capability://automation.workflow.neo4j.database.initialization.and.schema.management","name":"neo4j database initialization and schema management","description":"Provides database setup scripts that create the Neo4j schema (node labels, relationship types, indexes, constraints) required by ATLAS. Includes migration scripts for schema updates and database reset utilities for development. Scripts are idempotent and can be run multiple times without side effects. Supports both local Neo4j instances and cloud deployments (Neo4j Aura).","intents":["I want to set up a fresh Neo4j database for ATLAS without manual schema creation","I need to migrate my database schema when upgrading ATLAS versions","I want to reset my development database to a clean state"],"best_for":["developers setting up local ATLAS instances","DevOps engineers deploying ATLAS to production","teams managing multiple ATLAS environments (dev, staging, prod)"],"limitations":["Scripts are Neo4j-specific; no support for other graph databases","Migrations are manual; no automatic schema versioning or rollback","Schema changes require downtime if the server is running","No data migration helpers; schema changes that affect data require custom scripts"],"requires":["Neo4j 4.4+ instance with admin credentials","Node.js 18+ to run setup scripts","network access to Neo4j instance"],"input_types":["Neo4j connection parameters (URI, username, password)","optional: custom schema definitions"],"output_types":["created node labels, relationship types, indexes, constraints","migration logs and status"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Neo4j 4.4+ instance (local or cloud)","Node.js 18+","TypeScript 4.9+","neo4j driver package installed","MCP client implementation (e.g., Claude Desktop, custom MCP client)","Atlas MCP server running and accessible","Neo4j database configured and connected","logging library (Winston, Pino) configured","optional: error tracking service (Sentry, DataDog) for alerting","environment variables set before server startup"],"failure_modes":["Neo4j instance required — no embedded/in-memory fallback for local development","Cypher query complexity grows with deep hierarchies (5+ levels); no automatic query optimization","No built-in versioning of graph snapshots — requires external audit logging for change tracking","Tool discovery is static at server startup — new tools require server restart","No built-in pagination for list operations; large result sets returned in full","Tool input validation happens at MCP layer; no pre-flight checks for business logic constraints (e.g., circular dependencies)","Error messages are logged but not automatically sent to external services; requires integration","Sensitive data (passwords, tokens) must be manually filtered from logs","Log volume can be high with verbose logging; no built-in sampling or filtering","Error recovery is not automatic; agents must handle errors and retry","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3563300635710379,"quality":0.49,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T14:23:38.364Z","last_commit":"2025-07-22T05:17:41Z"},"community":{"stars":471,"forks":66,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-cyanheads-atlas-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=mcp-cyanheads-atlas-mcp-server"}},"signature":"/fUNtZ/tNIG+PW/5E0/KJJ4Wj//o2ref4jXvXinoa1gJPEm9KgRgDmaqM4YwuX2zHhSvS9hU436ysIqjGx1qCA==","signedAt":"2026-06-21T14:40:54.125Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-cyanheads-atlas-mcp-server","artifact":"https://unfragile.ai/mcp-cyanheads-atlas-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-cyanheads-atlas-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"}}