"Information about Neo4j graph database" matching MCP servers:

Enables LLMs to run complex graph algorithms on Neo4j databases, answering graph-related questions by selecting and executing appropriate parameterised graph algorithms.

Neo4j MCP Canary is a fast-moving, experimental release of the Neo4j MCP server for customers who want to explore emerging capabilities before they are considered for the official server.

This read-only MCP Server allows you to connect to Neo4J data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out our MCP Server for Neo4J (https://www.cdata.com/drivers/neo4j/download/mcp).

Enables LLM agents to access Global Database's company intelligence, including company profiles, financials, ownership, people search, prospecting, and KYB/compliance lookups against official registries.

A Model Context Protocol server that enables LLMs like Claude to interact with SQLite and SQL Server databases, allowing for schema inspection and SQL query execution.

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 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.

A server that provides access to Baidu Cloud Vector Database functionality through the Model Context Protocol, enabling LLM applications to perform vector searches and database operations via natural language.

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 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.

Gives AI assistants full control over Microsoft Access databases, enabling creation and management of forms, VBA, tables, queries, and more through natural language.

Enables semantic code search across multi-language codebases using natural language queries, integrated with Qdrant vector database for fast, cached retrieval.

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.