Knowledge Graph Server

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.