"Information about Neo4j graph database" matching MCP servers:

An implementation for managing Neo4j graph database operations through the Model Context Protocol, enabling users to execute Cypher queries against their Neo4j database via AI assistants like Cursor and Claude Desktop.

Graph-based tool retrieval for LLM agents. Builds a tool graph from OpenAPI/MCP specs and retrieves multi-step workflows via hybrid search (BM25 + graph traversal + embedding), recovering accuracy from 12% to 82% with 79% fewer tokens. Also works as an MCP Proxy to aggregate multiple servers behind 3 meta-tools.

Enables AI agents to orchestrate tasks as a DAG with automated validation loops, executing validation commands, tracking statuses, and allowing iterative code fixes until tasks pass.

Provides structured workflows (phases, gates, coordination) for AI agents, enabling complex task execution with quality enforcement and multi-agent coordination via Model Context Protocol.

A persistent memory implementation using a local knowledge graph that lets Claude remember information about users across conversations.

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 personal AI agents to fuse ambient multimodal perception with continuous episodic memory streams, routing routine reflexive actions at low latency while reserving heavier reasoning for ambiguous plans. Memory records apply Ebbinghaus recency decay with contradiction resolution and user-controlled deletion.

A Model Context Protocol server that provides real-time Caltrain schedule information, allowing AI assistants to look up train departures between any stations and access station information using GTFS data.

Provides AI assistants with real-time access to the AGNO framework documentation by enabling them to browse, search, and fetch documentation pages. This server allows users to query information about AGNO's Agents, Teams, and Workflows directly through MCP-compatible clients.

Enables agents to crawl and read websites politely, learning per host which reader, URL rewrite, or detail path actually reaches good information, and reusing those wins across runs or a shared database. Failures come back as coded errors with concrete next steps, so blocked or empty pages point to an honest alternative instead of endless retries.

MCP Server for OceanBase database and its tools

The AgentOps MCP server provides access to observability and tracing data for debugging complex AI agent runs. This adds crucial context about where the AI agent succeeds or fails.

Provides AI assistants with enhanced reasoning capabilities through structured thinking, persistent knowledge graph memory, and intelligent tool orchestration for complex problem-solving.

Enables Claude Code and Gemini CLI to see your macOS screen, answering questions about the file, error, UI, database table, or web page you're looking at. Captures screenshots, reads database schemas, and controls Chrome/Brave, presenting answers as on-screen highlights or spoken bubbles above your cursor.

A Model Context Protocol server that enables large language models to interact with Apache Superset databases through REST API, supporting database queries, table lookups, field information retrieval, and SQL execution.

Projects an Agent Surface (deterministic call graph) for any OpenAPI, enabling LLM agents to make correct, chain-of-calls interactions without guessing.

An on-prem knowledge graph for AI agents that uses typed entities and relationships, enabling persistent memory and cross-agent handoff via the Model Context Protocol.

A server that enables AI models to access user geolocation information via EdgeOne Pages Functions through the Model Context Protocol.

A transparent message ferry between Claude Desktop and Claude Code CLI, enabling task delegation with any MCP configuration without the bridge needing to know about specific MCPs.

Model Context Protocol (MCP) memory server built with Azure Functions and TypeScript, providing persistent knowledge graph storage for your preferred MCP client. Inspired by and forked from @modelcontextprotocol/server-memory

Enables LLMs to orchestrate AI agents and tasks, inspect DAG workflows, monitor cluster metrics, and perform safe database queries and statements through standardized MCP tools.

Enables autonomous multi-agent orchestration using directed acyclic graph workflows to interact with external developer infrastructure such as Git, Postgres, and Docker through safety-gated MCP servers.

Enables MCP-capable agents to read and write a shared local knowledge graph, preserving continuity across sessions and supporting handoffs, shared decisions, constraints, and live coordination. Exposes tools for searching, adding, linking, and querying context, plus task, lock, reminder, and graph-maintenance operations.

Enables Cursor agents to communicate via a shared chat room, allowing them to ask questions, share status, and warn about conflicts while collaborating on the same repo.

An MCP server that provides natural language interaction with Apache AGE graph databases, allowing users to query, visualize and manipulate graph data in PostgreSQL through Claude AI.

Enables MCP clients to discover and execute operational tools such as database mutations and system diagnostics over stdio, with deterministic structured outputs and Langfuse observability.

Connect your AI models to your favorite SurrealDB database, and let the LLMs do all your work for you.

Enables stateless human-in-the-loop DevOps automation, demonstrating MCP SEP-2322 multi-round trip requests for emergency database migration authorization.

Enables MCP hosts to run a user-configurable agent with multi-engine orchestration, declarative connectors to third-party APIs, task-graph execution, audit ledger, and memory, without needing to clone or configure multiple repositories.

Enables read-only access to Pocket Agent's product information, public persona templates, and app catalog. No authentication required.

Agent-first knowledge graph MCP server that provides 25 tools for managing a knowledge graph with nodes and edges, plus a human-readable dashboard for LLMs and AI agents.

Enables AI models to access user geolocation information through EdgeOne Pages Functions, allowing location-aware AI interactions.

A Server-Sent Events implementation using FastAPI framework that integrates Model Context Protocol (MCP), allowing AI models to access external tools and data sources like weather information.

MCP server that enables Claude Code to communicate with other Claude Code agents over HTTP, allowing users to ask questions about remote codebases or delegate coding tasks.

MCP server that lets multiple coding-agent sessions on the same machine discover each other and collaborate through a shared SQLite database.

An interface enabling high-frequency communication between AI tools (like Cursor and Windsurf) and users, allowing for option selection and information gathering through CLI, Web, or PyQt interfaces.

Agent network intelligence for trust verification, broker discovery, and capability matching. Ed25519 identity, graph-based trust scoring, USDC payments, and MCP tools for agent registration, search, and trust attestation.

A multi-server implementation that uses OpenAI's LLM to orchestrate multiple tool servers providing web search, weather lookup, random facts, and PostgreSQL database querying capabilities.

An MCP server that enables web search and document retrieval capabilities through Tavily API and LangConnect vector database, supporting AI agents in gathering information for comprehensive report generation.