{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-neo4j","slug":"neo4j","name":"Neo4j","type":"mcp","url":"https://github.com/neo4j-contrib/mcp-neo4j/","page_url":"https://unfragile.ai/neo4j","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-neo4j__cap_0","uri":"capability://tool.use.integration.cypher.query.execution.with.schema.aware.validation","name":"cypher query execution with schema-aware validation","description":"Executes Cypher queries against a Neo4j graph database with built-in schema introspection and validation. The MCP server parses incoming Cypher commands, validates them against the current database schema (nodes, relationships, properties), and returns structured results with type information. This prevents malformed queries from reaching the database and enables intelligent query suggestions based on available schema.","intents":["Execute read and write operations on graph data without manual query construction","Validate Cypher syntax and schema compatibility before execution","Retrieve query results in structured format for downstream processing","Debug graph queries by understanding schema constraints in real-time"],"best_for":["AI agents building knowledge graphs dynamically","LLM applications needing structured graph queries","Teams integrating Neo4j into multi-step reasoning workflows"],"limitations":["Query complexity is bounded by Neo4j's execution planner — very large graph traversals may timeout","No built-in query optimization hints; relies on Neo4j's cost-based planner","Schema validation happens at MCP layer, adding ~50-100ms latency per query","Cypher dialect limited to Neo4j 4.x+ syntax; no support for legacy 3.x queries"],"requires":["Neo4j database instance (4.0+) running and accessible","Network connectivity to Neo4j server (bolt:// or neo4j:// protocol)","MCP client compatible with tool-use protocol","Appropriate Neo4j user credentials with query execution permissions"],"input_types":["Cypher query string (plain text)","Query parameters as JSON object"],"output_types":["Structured result set (JSON array of records)","Query metadata (execution time, rows affected)","Error messages with line/column information"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-neo4j__cap_1","uri":"capability://memory.knowledge.graph.database.schema.introspection.and.discovery","name":"graph database schema introspection and discovery","description":"Automatically discovers and exposes the Neo4j database schema (node labels, relationship types, property keys, constraints, indexes) through MCP tools. The server queries Neo4j's internal schema metadata tables and constructs a machine-readable schema representation that LLMs can use to understand the graph structure without manual documentation. This enables context-aware query generation and prevents queries that reference non-existent entities.","intents":["Understand the structure of an unfamiliar graph database without reading documentation","Generate valid Cypher queries that reference only existing node labels and relationships","Discover available properties and constraints for a given entity type","Validate that a proposed query structure aligns with actual database schema"],"best_for":["LLM agents operating on unknown or evolving graph schemas","Teams onboarding new developers to graph database projects","Multi-tenant systems where schema varies per customer"],"limitations":["Schema discovery is point-in-time; does not track schema changes in real-time","Large schemas (1000+ node types) may take 2-5 seconds to fully introspect","Does not expose Neo4j stored procedures or user-defined functions","Schema metadata is read-only; cannot modify schema through MCP interface"],"requires":["Neo4j database with APOC library installed (for advanced introspection)","User account with SHOW SCHEMA privileges","Network access to Neo4j instance"],"input_types":["Optional filter parameters (e.g., label name, relationship type)"],"output_types":["JSON schema definition with node labels, relationships, properties","Constraint and index metadata","Property cardinality and type hints"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-neo4j__cap_2","uri":"capability://memory.knowledge.graph.backed.memory.and.context.persistence","name":"graph-backed memory and context persistence","description":"Stores conversation history, entity relationships, and reasoning state in Neo4j as a persistent knowledge graph. Each conversation turn creates nodes for entities, intents, and decisions; relationships capture dependencies and temporal ordering. The MCP server retrieves relevant subgraphs on each turn to provide LLMs with long-term context without token overhead. This enables multi-turn conversations where the agent remembers prior decisions and can reason over accumulated knowledge.","intents":["Maintain conversation context across multiple turns without exhausting token limits","Build a persistent knowledge graph of entities and relationships mentioned in conversations","Retrieve relevant prior context based on semantic similarity or graph distance","Enable agents to reason over accumulated facts and decisions from previous interactions"],"best_for":["Long-running conversational agents that need memory beyond context window","Multi-agent systems where agents share a common knowledge graph","Applications requiring audit trails of reasoning and decisions"],"limitations":["Requires manual schema design for conversation entities and relationships","Graph traversal for context retrieval adds 100-300ms per turn","No automatic deduplication of entities; requires application logic to merge duplicates","Memory grows unbounded; requires periodic cleanup or archival strategy"],"requires":["Neo4j instance with write permissions","Application code to define conversation entity schema","Strategy for entity linking (mapping text mentions to graph nodes)"],"input_types":["Conversation turn (user message, assistant response)","Entity extraction results (entities and relationships to persist)"],"output_types":["Persisted conversation nodes and relationships","Retrieved context subgraph (relevant prior turns and entities)","Graph statistics (entity frequency, relationship patterns)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-neo4j__cap_3","uri":"capability://planning.reasoning.multi.step.reasoning.with.graph.based.state.tracking","name":"multi-step reasoning with graph-based state tracking","description":"Tracks intermediate reasoning steps, hypotheses, and decision branches as nodes and relationships in the graph. Each reasoning step creates a node with the step content, confidence, and dependencies; relationships capture causal chains and alternative paths. The MCP server enables agents to query the reasoning graph to backtrack, explore alternatives, or explain decisions. This provides transparency into agent reasoning and enables recovery from incorrect intermediate conclusions.","intents":["Track and visualize multi-step reasoning chains for debugging and explanation","Explore alternative reasoning paths without restarting from scratch","Identify and correct incorrect intermediate conclusions","Build explainable AI systems that can justify decisions by tracing reasoning graphs"],"best_for":["Complex reasoning agents where transparency is critical","Systems requiring audit trails of decision-making","Research applications studying agent reasoning patterns"],"limitations":["Reasoning graph size grows exponentially with branching factor; requires pruning strategy","Querying large reasoning graphs (1000+ steps) may be slow without proper indexing","No built-in visualization; requires external tools to render reasoning graphs","Requires application logic to define what constitutes a 'reasoning step'"],"requires":["Neo4j instance with write permissions","Application code to instrument reasoning steps","Strategy for pruning or archiving old reasoning branches"],"input_types":["Reasoning step description (text)","Confidence score (0-1)","Parent step ID (for dependency tracking)"],"output_types":["Reasoning graph (nodes and relationships)","Path queries (reasoning chains from start to conclusion)","Alternative paths (unexplored branches)"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-neo4j__cap_4","uri":"capability://data.processing.analysis.dynamic.knowledge.graph.construction.from.unstructured.text","name":"dynamic knowledge graph construction from unstructured text","description":"Accepts unstructured text (documents, conversation turns, web content) and constructs graph nodes and relationships by extracting entities and their relationships. The MCP server provides tools for entity extraction, relationship inference, and graph construction that LLMs can call iteratively. Extracted entities are deduplicated against existing graph nodes using fuzzy matching or semantic similarity, and new relationships are merged with existing ones to build a unified knowledge graph.","intents":["Convert unstructured documents into queryable graph structure","Build knowledge graphs incrementally from multiple text sources","Extract and deduplicate entities across documents","Infer implicit relationships between entities mentioned in text"],"best_for":["Document processing pipelines that need structured output","Knowledge base construction from heterogeneous sources","Applications combining multiple text sources into unified knowledge graph"],"limitations":["Entity extraction quality depends on LLM capability; hallucinations create spurious nodes","Deduplication is probabilistic; requires manual review for critical applications","Relationship inference is limited to patterns the LLM can recognize","No built-in handling of contradictory information from multiple sources"],"requires":["LLM with entity extraction capability (via prompt or fine-tuning)","Neo4j instance with write permissions","Strategy for entity deduplication (fuzzy matching, semantic similarity, or manual review)"],"input_types":["Unstructured text (document, paragraph, sentence)","Optional entity type hints (to guide extraction)"],"output_types":["Extracted entities (with types and properties)","Inferred relationships (with confidence scores)","Graph update operations (create/merge nodes and relationships)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-neo4j__cap_5","uri":"capability://search.retrieval.semantic.search.over.graph.entities.using.embeddings","name":"semantic search over graph entities using embeddings","description":"Integrates vector embeddings with graph structure to enable semantic search over entities and relationships. The MCP server stores embeddings for entity descriptions or properties, and provides a search tool that finds semantically similar entities using vector similarity while respecting graph structure (e.g., 'find entities similar to X that are connected to Y'). This combines the expressiveness of graph queries with the flexibility of semantic search.","intents":["Find entities similar to a query without exact keyword matching","Discover related entities based on semantic meaning","Combine semantic search with graph structure constraints","Build recommendation systems that traverse semantic and structural relationships"],"best_for":["Applications with fuzzy entity matching requirements","Recommendation systems combining semantic and structural similarity","Knowledge graphs with natural language entity descriptions"],"limitations":["Requires embedding model (local or API-based); adds latency and cost","Embedding quality depends on model choice; domain-specific models may be needed","Hybrid queries (semantic + structural) are slower than pure graph queries","Embeddings must be updated when entity descriptions change"],"requires":["Embedding model (e.g., OpenAI, Hugging Face, local model)","Neo4j instance with vector index support (4.4+)","Application code to generate and store embeddings"],"input_types":["Query text (natural language description)","Optional graph constraints (e.g., node label, relationship type)"],"output_types":["Ranked list of similar entities (with similarity scores)","Subgraph of related entities and relationships"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-neo4j__cap_6","uri":"capability://tool.use.integration.transactional.write.operations.with.acid.guarantees","name":"transactional write operations with acid guarantees","description":"Executes write operations (CREATE, UPDATE, DELETE) as ACID transactions, ensuring consistency even when multiple agents or processes modify the graph concurrently. The MCP server wraps Cypher write queries in Neo4j transactions, handles conflicts and retries, and provides rollback capabilities. This prevents data corruption and race conditions in multi-agent systems where multiple LLM instances may modify the same graph.","intents":["Safely update graph data from multiple concurrent agents","Ensure consistency when creating related entities and relationships","Rollback changes if subsequent operations fail","Prevent duplicate entity creation in concurrent scenarios"],"best_for":["Multi-agent systems sharing a common knowledge graph","Applications requiring strong consistency guarantees","Systems with high concurrency where race conditions are likely"],"limitations":["Transactions add overhead; write latency increases by 50-200ms per transaction","Long-running transactions may block other writers; requires careful query design","Deadlocks are possible with complex concurrent access patterns","Rollback requires application logic to handle failed transactions"],"requires":["Neo4j instance (4.0+) with transaction support","Write permissions on target nodes and relationships","Application code to handle transaction failures and retries"],"input_types":["Cypher write query (CREATE, MERGE, SET, DELETE)","Optional transaction timeout (in seconds)"],"output_types":["Transaction result (nodes/relationships created/updated/deleted)","Transaction ID (for audit trails)","Error details if transaction fails"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-neo4j__cap_7","uri":"capability://search.retrieval.relationship.pattern.matching.and.graph.traversal","name":"relationship pattern matching and graph traversal","description":"Executes complex graph traversal queries that match relationship patterns and navigate multi-hop paths. The MCP server provides tools for common traversal patterns (shortest path, all paths, k-hop neighbors) and allows custom Cypher patterns for domain-specific queries. This enables agents to discover indirect relationships, identify clusters, and reason over graph topology.","intents":["Find shortest path between two entities","Discover all paths connecting entities (for relationship analysis)","Retrieve k-hop neighbors of an entity","Identify clusters or communities in the graph","Reason over indirect relationships and transitive properties"],"best_for":["Applications requiring relationship discovery and analysis","Recommendation systems based on graph proximity","Fraud detection systems analyzing transaction networks","Knowledge graph reasoning over indirect relationships"],"limitations":["Path-finding queries are computationally expensive; may timeout on large graphs","Shortest path queries require index on relationship types for performance","All-paths queries can return exponentially many results; requires result limiting","Graph algorithms (clustering, centrality) require APOC or GDS library"],"requires":["Neo4j instance with relationship indexes (for performance)","Cypher knowledge or LLM capability to construct traversal patterns","Optional: Neo4j Graph Data Science library for advanced algorithms"],"input_types":["Start and end node identifiers","Optional relationship type filters","Optional maximum path length"],"output_types":["Path results (sequence of nodes and relationships)","Path statistics (length, relationship types)","Subgraph of traversed nodes and relationships"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-neo4j__cap_8","uri":"capability://safety.moderation.constraint.enforcement.and.data.validation","name":"constraint enforcement and data validation","description":"Defines and enforces constraints on graph data (uniqueness, existence, type constraints) to maintain data quality. The MCP server exposes Neo4j's constraint system, allowing agents to define constraints declaratively and validate data before writes. Constraints prevent invalid states (e.g., duplicate entities, missing required properties) and provide clear error messages when violations occur.","intents":["Prevent duplicate entity creation","Enforce required properties on entities","Validate data types and value ranges","Maintain referential integrity in the graph"],"best_for":["Applications requiring strict data quality","Multi-agent systems where consistency is critical","Regulated industries with compliance requirements"],"limitations":["Constraint violations cause write failures; requires application error handling","Complex validation logic cannot be expressed as constraints; requires application code","Constraints are defined at schema level; cannot be dynamic per-transaction","Performance impact of constraint checking is minimal but non-zero"],"requires":["Neo4j instance (4.0+) with constraint support","Schema design that anticipates validation requirements","Application code to handle constraint violations"],"input_types":["Constraint definition (property, node label, relationship type)","Constraint type (UNIQUE, EXISTS, TYPE)"],"output_types":["Constraint creation confirmation","Validation errors with constraint details"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Neo4j database instance (4.0+) running and accessible","Network connectivity to Neo4j server (bolt:// or neo4j:// protocol)","MCP client compatible with tool-use protocol","Appropriate Neo4j user credentials with query execution permissions","Neo4j database with APOC library installed (for advanced introspection)","User account with SHOW SCHEMA privileges","Network access to Neo4j instance","Neo4j instance with write permissions","Application code to define conversation entity schema","Strategy for entity linking (mapping text mentions to graph nodes)"],"failure_modes":["Query complexity is bounded by Neo4j's execution planner — very large graph traversals may timeout","No built-in query optimization hints; relies on Neo4j's cost-based planner","Schema validation happens at MCP layer, adding ~50-100ms latency per query","Cypher dialect limited to Neo4j 4.x+ syntax; no support for legacy 3.x queries","Schema discovery is point-in-time; does not track schema changes in real-time","Large schemas (1000+ node types) may take 2-5 seconds to fully introspect","Does not expose Neo4j stored procedures or user-defined functions","Schema metadata is read-only; cannot modify schema through MCP interface","Requires manual schema design for conversation entities and relationships","Graph traversal for context retrieval adds 100-300ms per turn","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"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":"2026-06-17T09:51:03.579Z","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=neo4j","compare_url":"https://unfragile.ai/compare?artifact=neo4j"}},"signature":"OpnedlxufL4kbgORtR3WFtOIGRyvJUpwfJyX+p2G9Dj+++xQhog2+l2YzIOrXf4HcoQ5Z7JxCN/gvtkVdsXPDg==","signedAt":"2026-06-20T09:25:14.258Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/neo4j","artifact":"https://unfragile.ai/neo4j","verify":"https://unfragile.ai/api/v1/verify?slug=neo4j","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"}}