{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_aiuluna-knowledge-graph-mcp","slug":"aiuluna-knowledge-graph-mcp","name":"Knowledge Graph Server","type":"mcp","url":"https://github.com/aiuluna/knowledge-graph-mcp#readme","page_url":"https://unfragile.ai/aiuluna-knowledge-graph-mcp","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:aiuluna/knowledge-graph-mcp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_aiuluna-knowledge-graph-mcp__cap_0","uri":"capability://tool.use.integration.mcp.native.knowledge.graph.query.interface","name":"mcp-native knowledge graph query interface","description":"Exposes knowledge graph operations through the Model Context Protocol (MCP) resource and tool abstractions, allowing AI assistants to query and traverse graph structures using standardized MCP request/response patterns. Implements MCP server lifecycle management with resource discovery, enabling assistants to introspect available graphs and their schemas before querying. Uses MCP's tool calling mechanism to bind graph operations (traversal, filtering, aggregation) to LLM function calls with structured input/output schemas.","intents":["I want my AI assistant to query a knowledge graph without custom API integration","I need to expose graph operations as discoverable MCP tools that Claude or other MCP clients can invoke","I want to standardize how multiple AI assistants interact with the same knowledge graph backend"],"best_for":["teams building MCP-compatible AI agents and assistants","developers integrating knowledge graphs into Claude, Cursor, or other MCP-aware tools","organizations standardizing LLM-to-knowledge-graph communication protocols"],"limitations":["MCP protocol overhead adds ~50-100ms per request compared to direct library calls","Limited to MCP client capabilities — complex graph algorithms must be pre-computed server-side","No built-in authentication beyond MCP transport layer — relies on host process security"],"requires":["MCP-compatible client (Claude, Cursor, or custom MCP consumer)","Node.js 16+ or Python 3.8+ (depending on server implementation)","Knowledge graph data source (Neo4j, RDF store, or in-memory graph)"],"input_types":["SPARQL queries (for RDF graphs)","Cypher queries (for property graphs)","JSON-structured graph traversal requests","Natural language queries (translated by LLM to graph queries)"],"output_types":["JSON graph results with node/edge metadata","Structured query results with schema validation","Resource descriptions for MCP resource discovery"],"categories":["tool-use-integration","mcp-servers"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aiuluna-knowledge-graph-mcp__cap_1","uri":"capability://data.processing.analysis.multi.graph.type.data.model.abstraction","name":"multi-graph-type data model abstraction","description":"Abstracts over multiple knowledge graph representations (topologies, timelines, ontologies) through a unified internal data model, allowing a single server to manage heterogeneous graph types with type-specific query and visualization semantics. Implements graph-type-specific traversal logic and schema validation, enabling timeline graphs to support temporal ordering constraints while ontology graphs enforce class hierarchies and property restrictions. Routes queries to appropriate backend engines based on graph type metadata.","intents":["I need to store and query different types of graphs (dependency graphs, timelines, taxonomies) in a single system","I want type-specific constraints enforced (e.g., temporal ordering for timelines, class inheritance for ontologies)","I need to switch between graph representations without rewriting query logic"],"best_for":["knowledge management systems handling mixed graph types","semantic web applications combining RDF ontologies with temporal and topological data","research tools analyzing both structural and temporal relationships"],"limitations":["Type-specific constraints add query planning overhead — complex multi-type queries may require manual optimization","No automatic type inference — graphs must be explicitly typed at creation","Cross-type joins require explicit mapping logic, not automatically derived from schema"],"requires":["Graph data source supporting multiple representations (or separate backends per type)","Schema definitions for each graph type (ontology, timeline structure, topology constraints)","Knowledge of graph type semantics to design appropriate queries"],"input_types":["Graph type specification (topology, timeline, ontology)","Schema definitions (OWL for ontologies, temporal constraints for timelines)","Node/edge data in format matching graph type"],"output_types":["Type-specific query results with schema-validated properties","Visualization metadata (layout hints for topologies, temporal ordering for timelines)","Type-aware aggregations and statistics"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aiuluna-knowledge-graph-mcp__cap_10","uri":"capability://search.retrieval.graph.search.with.semantic.similarity.and.ranking","name":"graph search with semantic similarity and ranking","description":"Enables semantic search over graph nodes using embeddings or similarity metrics, ranking results by relevance rather than exact matching. Supports full-text search on node properties combined with graph structure (e.g., find similar concepts near a given node). Integrates with embedding models (local or API-based) to compute semantic similarity, with caching for performance. Supports filtering results by graph topology or metadata.","intents":["I want to find concepts similar to 'machine learning' in my ontology","I need to search for events in a timeline by semantic meaning, not just keywords","I want to find related nodes without knowing exact property values"],"best_for":["knowledge discovery and exploration interfaces","semantic search applications over large graphs","recommendation systems based on graph structure and similarity"],"limitations":["Embedding computation adds latency — ~100-500ms per search depending on model size","Embedding quality depends on model training — may not capture domain-specific semantics","Caching embeddings requires storage — scales with graph size"],"requires":["Embedding model (local or API-based like OpenAI, Hugging Face)","Vector similarity computation (cosine, Euclidean, etc.)","Optional: vector index (FAISS, Annoy) for fast similarity search"],"input_types":["Search query (text or node reference)","Similarity metric (cosine, Euclidean, etc.)","Filters (topology, metadata, property constraints)"],"output_types":["Ranked list of similar nodes with similarity scores","Subgraphs of similar nodes with relationships","Search explanations showing why results are relevant"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aiuluna-knowledge-graph-mcp__cap_2","uri":"capability://image.visual.graph.visualization.and.layout.generation","name":"graph visualization and layout generation","description":"Generates visualization metadata and layout coordinates for knowledge graphs, supporting topology-specific rendering (hierarchical layouts for DAGs, circular layouts for cycles, temporal axis for timelines). Computes layout algorithms server-side and returns coordinates/styling hints that clients can render without re-computing, reducing client-side complexity. Supports multiple layout strategies (force-directed, hierarchical, radial) selected based on graph type and structure.","intents":["I want to visualize a knowledge graph without implementing layout algorithms on the client","I need different visualization layouts for different graph types (hierarchical for ontologies, temporal for timelines)","I want to generate static graph images or interactive visualizations from server-side data"],"best_for":["web applications displaying knowledge graphs to end users","documentation systems visualizing ontologies and taxonomies","analysis tools showing temporal relationships and dependency chains"],"limitations":["Layout computation scales with O(n²) complexity for force-directed algorithms — large graphs (>1000 nodes) may require sampling or hierarchical layout","No real-time layout updates — clients must re-request layout after graph modifications","Limited customization of visual styling — relies on client-side CSS/theming for full control"],"requires":["Graph structure with node/edge data","Layout algorithm library (Graphviz, D3.js force simulation, or custom implementation)","Client capable of rendering SVG or canvas-based visualizations"],"input_types":["Graph structure (nodes and edges with metadata)","Layout strategy specification (hierarchical, force-directed, radial)","Visualization preferences (node colors, edge styles, label placement)"],"output_types":["JSON layout data with node coordinates and dimensions","SVG or canvas rendering instructions","Interactive visualization metadata (zoom levels, pan constraints)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aiuluna-knowledge-graph-mcp__cap_3","uri":"capability://memory.knowledge.version.tracking.and.resource.state.management","name":"version tracking and resource state management","description":"Maintains version history for knowledge graph resources (nodes, edges, schemas) with snapshot-based or delta-based versioning, enabling rollback to previous states and audit trails of modifications. Implements resource lifecycle tracking (created, modified, deprecated) with metadata timestamps and change attribution. Provides version comparison and diff operations to identify what changed between versions, supporting both structural changes (node/edge additions) and property mutations.","intents":["I need to track who changed what in the knowledge graph and when","I want to rollback a graph to a previous version after accidental modifications","I need to audit the evolution of a knowledge graph over time"],"best_for":["collaborative knowledge management systems with multiple editors","compliance-heavy domains requiring audit trails (legal, medical, financial)","research projects tracking the evolution of ontologies and taxonomies"],"limitations":["Version storage grows linearly with change frequency — large graphs with frequent updates require aggressive pruning or archival","Diff computation for large graphs can be expensive — may require indexing or sampling for performance","No built-in conflict resolution for concurrent edits — requires external coordination or optimistic locking"],"requires":["Persistent storage backend supporting versioned data (Git-like, database with temporal tables, or append-only log)","Metadata schema for tracking change attribution and timestamps","Mechanism for identifying and comparing graph snapshots"],"input_types":["Graph modifications (node/edge additions, deletions, property updates)","Version identifiers or timestamps for rollback/comparison","Change metadata (author, timestamp, change description)"],"output_types":["Version history with metadata (timestamps, authors, change summaries)","Diff results showing added/removed/modified resources","Rollback confirmations and restored graph state"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aiuluna-knowledge-graph-mcp__cap_4","uri":"capability://memory.knowledge.comprehensive.resource.management.and.discovery","name":"comprehensive resource management and discovery","description":"Implements a resource registry that catalogs all knowledge graph entities (graphs, nodes, edges, schemas) with metadata (type, creation date, modification date, tags, descriptions) and enables efficient discovery through filtering, searching, and enumeration. Exposes resources through MCP's resource abstraction, allowing clients to discover available graphs and their schemas before querying. Manages resource lifecycle (creation, updates, deletion) with consistency guarantees and optional soft-delete support for audit trails.","intents":["I want to discover what graphs and schemas are available in the system without hardcoding resource names","I need to search for specific nodes or edges across multiple graphs by metadata or content","I want to manage the lifecycle of graph resources (create, update, deprecate, delete) with audit trails"],"best_for":["multi-tenant knowledge graph systems with dynamic resource creation","MCP-based AI assistants that need to discover available graphs before querying","knowledge management platforms with complex resource hierarchies"],"limitations":["Resource enumeration can be slow for systems with millions of resources — requires pagination and indexing","Metadata search requires full-text indexing or regex matching — may not scale to very large catalogs without optimization","No built-in access control — relies on host process or external authorization layer"],"requires":["Metadata storage backend (database, file system, or in-memory index)","Resource naming/URI scheme for unique identification","Filtering and search implementation (SQL, Elasticsearch, or custom indexing)"],"input_types":["Resource creation requests with metadata (name, type, description, tags)","Filter/search queries (by type, tag, creation date, modification date)","Resource update/deletion requests"],"output_types":["Resource listings with metadata and URIs","Search results ranked by relevance","Resource status and lifecycle information"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aiuluna-knowledge-graph-mcp__cap_5","uri":"capability://data.processing.analysis.schema.validation.and.constraint.enforcement","name":"schema validation and constraint enforcement","description":"Validates incoming graph data against defined schemas (ontology class definitions, property restrictions, type constraints) before insertion, rejecting invalid data and providing detailed error messages. Enforces constraints at write time (cardinality restrictions, property types, required fields) and optionally at query time (filtering invalid results). Supports multiple schema languages (OWL for ontologies, JSON Schema for property graphs, custom constraint DSLs) with pluggable validators.","intents":["I want to ensure data quality by rejecting invalid nodes/edges before they enter the graph","I need to enforce domain-specific constraints (e.g., a person must have a name, a timeline event must have a date)","I want to provide clear error messages when data violates the schema"],"best_for":["data-intensive knowledge graphs requiring strict consistency","semantic web applications using OWL ontologies","collaborative systems where multiple users contribute data"],"limitations":["Schema validation adds latency to write operations — complex schemas with many constraints may require optimization","No automatic schema inference — schemas must be manually defined or imported","Constraint violations are detected at write time, not at query time — invalid data already in the graph cannot be retroactively validated"],"requires":["Schema definition in supported language (OWL, JSON Schema, or custom DSL)","Validation engine implementation for chosen schema language","Error reporting mechanism with detailed constraint violation messages"],"input_types":["Node/edge data to be validated","Schema definitions (OWL, JSON Schema, constraint rules)","Validation configuration (strict vs. lenient, custom error handlers)"],"output_types":["Validation success/failure status","Detailed error messages with constraint violations","Suggestions for fixing invalid data"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aiuluna-knowledge-graph-mcp__cap_6","uri":"capability://search.retrieval.graph.traversal.and.path.finding.operations","name":"graph traversal and path-finding operations","description":"Implements efficient graph traversal algorithms (BFS, DFS, shortest path, all-paths) with support for weighted edges, directed/undirected graphs, and custom traversal predicates. Enables filtering during traversal (e.g., only follow edges of certain types, stop at nodes matching criteria) to reduce result sets and improve performance. Returns paths with full node/edge metadata, enabling clients to reconstruct the traversal path and analyze relationships between distant nodes.","intents":["I want to find the shortest path between two nodes in a knowledge graph","I need to traverse a graph following specific edge types or node properties","I want to find all paths between two nodes that satisfy certain constraints"],"best_for":["recommendation systems finding connections between entities","dependency analysis tools tracing relationships through complex systems","semantic search systems finding related concepts through graph traversal"],"limitations":["All-paths queries can explode exponentially in dense graphs — requires depth limits or result sampling","Traversal performance degrades with graph size — large graphs (>100K nodes) may require indexing or sampling","Custom traversal predicates require server-side execution — cannot be fully offloaded to client"],"requires":["Graph data structure with edge connectivity information","Traversal algorithm implementations (BFS, DFS, Dijkstra, etc.)","Optional: edge weights for shortest-path calculations"],"input_types":["Start and end node identifiers","Traversal constraints (edge types, node properties, depth limits)","Path filtering predicates (custom functions or declarative rules)"],"output_types":["Path results with full node/edge metadata","Path statistics (length, total weight, traversal cost)","Multiple paths ranked by relevance or distance"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aiuluna-knowledge-graph-mcp__cap_7","uri":"capability://data.processing.analysis.graph.aggregation.and.statistical.analysis","name":"graph aggregation and statistical analysis","description":"Computes aggregate statistics over graph subsets (node counts, edge densities, clustering coefficients, centrality measures) with support for grouping by node/edge properties. Enables filtering aggregations by graph structure (e.g., count nodes reachable within N hops) or metadata (e.g., count nodes created in a date range). Returns aggregation results with optional breakdowns by property values, enabling exploratory analysis of graph structure and content.","intents":["I want to understand the structure of my knowledge graph (how many nodes, edges, connected components)","I need to identify important nodes using centrality measures (PageRank, betweenness, degree)","I want to analyze how the graph has grown over time or by category"],"best_for":["knowledge graph quality assessment and monitoring","exploratory analysis of large graphs","reporting and dashboarding systems visualizing graph statistics"],"limitations":["Centrality computation scales with O(n²) or O(n³) complexity — large graphs require sampling or approximation","Aggregations require full graph scans unless pre-computed and cached","No real-time aggregation updates — statistics become stale as graph evolves"],"requires":["Graph data structure with full connectivity information","Statistical computation libraries (NetworkX, custom implementations)","Optional: caching layer for pre-computed statistics"],"input_types":["Graph subset specification (all nodes, filtered by property, within N hops)","Aggregation type (count, sum, average, centrality measure)","Grouping dimensions (by node type, by property value, by creation date)"],"output_types":["Aggregate statistics (counts, sums, averages, percentiles)","Centrality scores for individual nodes","Breakdowns by property values or time periods"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aiuluna-knowledge-graph-mcp__cap_8","uri":"capability://automation.workflow.collaborative.graph.editing.with.conflict.resolution","name":"collaborative graph editing with conflict resolution","description":"Supports concurrent edits from multiple users/agents with conflict detection and resolution strategies (last-write-wins, custom merge logic, or manual resolution). Tracks edit origins (user, timestamp, change description) and provides conflict reports showing incompatible mutations. Implements operational transformation or CRDT-style approaches to enable offline editing with eventual consistency.","intents":["I want multiple team members to edit the knowledge graph simultaneously without overwriting each other's changes","I need to understand and resolve conflicts when two people edit the same node or relationship","I want to support offline editing with automatic synchronization when reconnected"],"best_for":["collaborative knowledge management platforms with multiple editors","distributed teams working on shared knowledge bases","systems requiring offline-first capabilities with eventual consistency"],"limitations":["Conflict resolution adds latency to write operations — ~50-200ms per mutation depending on strategy","Complex conflicts (e.g., incompatible schema changes) may require manual intervention — no automatic resolution","CRDT or OT implementations add complexity and memory overhead — not suitable for extremely large graphs"],"requires":["Conflict detection and resolution strategy (last-write-wins, custom logic, or manual)","Distributed storage or message queue for coordinating edits across clients","Client-side buffering for offline edits if offline support needed"],"input_types":["Concurrent mutations from multiple sources","Conflict resolution policies (merge strategy, priority rules)","Edit metadata (user, timestamp, description)"],"output_types":["Merged graph state after conflict resolution","Conflict reports with details of incompatible changes","Audit trail showing how conflicts were resolved"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aiuluna-knowledge-graph-mcp__cap_9","uri":"capability://planning.reasoning.graph.reasoning.and.inference","name":"graph reasoning and inference","description":"Applies logical inference rules to derive new facts from existing graph data, supporting forward chaining (derive new facts from rules) and backward chaining (query-driven inference). Implements ontology-based reasoning (RDFS, OWL-lite subset) for class hierarchies and property inheritance, plus custom rule engines for domain-specific inference. Caches inferred facts for performance while maintaining consistency with base facts.","intents":["I want to automatically infer that a person is a grandparent based on parent relationships","I need to classify entities based on ontology rules (e.g., a thing is a Vehicle if it has wheels and an engine)","I want to find implicit relationships not explicitly stored in the graph"],"best_for":["semantic web applications using OWL/RDFS ontologies","knowledge graphs requiring automated reasoning and classification","systems deriving insights from implicit relationships"],"limitations":["Inference can be computationally expensive — forward chaining may derive millions of facts for complex rule sets","Inference caching becomes stale after mutations — requires invalidation and recomputation","Circular rule dependencies or infinite derivations require careful rule design — no automatic cycle detection"],"requires":["Rule definitions (SPARQL CONSTRUCT, Rete algorithm rules, or custom DSL)","Ontology or schema defining inference semantics","Sufficient computational resources for inference execution"],"input_types":["Base graph facts (nodes, edges)","Inference rules (forward-chaining or backward-chaining)","Query patterns for backward-chaining inference"],"output_types":["Inferred facts (new nodes, edges, or properties)","Inference traces showing derivation paths","Confidence scores if probabilistic reasoning supported"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"high","permissions":["MCP-compatible client (Claude, Cursor, or custom MCP consumer)","Node.js 16+ or Python 3.8+ (depending on server implementation)","Knowledge graph data source (Neo4j, RDF store, or in-memory graph)","Graph data source supporting multiple representations (or separate backends per type)","Schema definitions for each graph type (ontology, timeline structure, topology constraints)","Knowledge of graph type semantics to design appropriate queries","Embedding model (local or API-based like OpenAI, Hugging Face)","Vector similarity computation (cosine, Euclidean, etc.)","Optional: vector index (FAISS, Annoy) for fast similarity search","Graph structure with node/edge data"],"failure_modes":["MCP protocol overhead adds ~50-100ms per request compared to direct library calls","Limited to MCP client capabilities — complex graph algorithms must be pre-computed server-side","No built-in authentication beyond MCP transport layer — relies on host process security","Type-specific constraints add query planning overhead — complex multi-type queries may require manual optimization","No automatic type inference — graphs must be explicitly typed at creation","Cross-type joins require explicit mapping logic, not automatically derived from schema","Embedding computation adds latency — ~100-500ms per search depending on model size","Embedding quality depends on model training — may not capture domain-specific semantics","Caching embeddings requires storage — scales with graph size","Layout computation scales with O(n²) complexity for force-directed algorithms — large graphs (>1000 nodes) may require sampling or hierarchical layout","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.57,"ecosystem":0.48999999999999994,"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":"2026-05-24T12:16:25.635Z","last_scraped_at":"2026-05-03T15:18:44.267Z","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=aiuluna-knowledge-graph-mcp","compare_url":"https://unfragile.ai/compare?artifact=aiuluna-knowledge-graph-mcp"}},"signature":"RzKOM0Qe3z8OhWfJ8iPdcWk9yRDG82M+fJziQq/c2jv0ZOVlaybLcjjoaPuHznBgYNtrg1dIFH7whabzQP5xBA==","signedAt":"2026-06-19T23:50:08.642Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/aiuluna-knowledge-graph-mcp","artifact":"https://unfragile.ai/aiuluna-knowledge-graph-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=aiuluna-knowledge-graph-mcp","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"}}