"Dgraph" matching MCP servers:

Self-hostable MCP server for WhisperGraph — a graph of 7.39B nodes / 39B edges mapping DNS, BGP, GeoIP, WHOIS, and threat intelligence. Six read-only tools (Cypher query + schema introspection + threat assessment), six resources, eight investigation prompts. stdio and Streamable HTTP transports.

Official draw.io MCP server: create diagrams from Mermaid, XML or CSV, shown inline in Claude, ChatGPT, VS Code and Cursor or opened in the draw.io editor. Searches 10,000+ shapes.

Gives AI agents instant access to software architecture, dependencies, and impact analysis through pre-computed sgraph models, replacing dozens of grep/read cycles with a single tool call.

Enables agents to build and query structured semantic graphs, extracting, traversing and multi-hop reasoning over entity-relation-object triples while producing Cypher and JSON-LD graph output. It also combines BM25 lexical and dense vector retrieval with rank fusion, semantic caching and context reordering for grounded agentic memory.

Enables extraction of subject-predicate-object triples from text and multi-hop reasoning over connected data, producing Cypher and JSON-LD outputs for agent memory, vector search, and retrieval-augmented generation workflows.

Enables cryptographic chunk hashing, reverse claim-to-source attribution, clearance boundary validation, and provenance audit trails for enterprise knowledge graphs to combat hallucinations.

Enables graph pathfinding and network analysis through MCP, including Dijkstra/A* shortest paths, topological dependency resolution, maximum flow, minimum spanning trees, and PageRank centrality for agent planning.

Enables AI agents to combine ambient multimodal perception, continuous episodic memory, and recency-decay context briefing for fast deterministic decisions and safe tool routing. It integrates with MCP clients to reduce unnecessary frontier LLM calls while enforcing token budgets and local-first memory retention.

Enables agents to compute shortest paths with Dijkstra and A*, resolve DAG dependencies, solve maximum flow, build minimum spanning trees, and rank graph centrality using JSON-RPC MCP tools.

Remote MCP server grounding GTM agents in governed, cited company truth, enabling AI agents to use verified context, search, validate content, and generate grounded drafts.

Provides AI agents with persistent memory and knowledge management through a comprehensive knowledge graph platform. Enables storing, searching, and managing entities, relationships, and observations with advanced features like trending analysis and smart ranking.

Enables deep research evidence synthesis and citation verification by generating structured research graphs through the Model Context Protocol.

Enables AI agents and MCP-compliant clients to extract visual data from charts and graphs into structured markdown tables using deterministic, zero-dependency Python.

Enables AI agents to synthesize and update hierarchical episodic and semantic user profile memory graphs through MCP, using deterministic zero-dependency Python processing with JSON Schema validation.

Enables AI agents to analyze and link citations across multiple research sources, building cross-citation graphs and resolving consensus from conflicting findings. It exposes this capability as a deterministic, zero-dependency MCP server that plugs into Claude Desktop, Cursor, and other MCP-compliant clients.

Enables querying a Neo4j knowledge graph about Wroclaw University of Science and Technology using natural language. Converts user questions into Cypher queries and retrieves contextual information through an intelligent RAG pipeline with LLM-powered query routing.

Enables agents to compile a personal knowledge vault into a linked graph by indexing bi-directional links and parsing [[wikilinks]], tags, and unlinked mentions across notes. It runs deterministically over MCP without external dependencies, turning raw note collections into navigable, queryable knowledge structures.