"Information about Neo4j graph database" matching MCP servers:

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.

A graph-based MCP server that provides AI coding agents with persistent memory to store patterns, track complex relationships, and retrieve knowledge across sessions. It leverages graph structures to handle temporal queries and relational paths that traditional vector stores often miss.

Indexes local Python code into a Neo4j graph database to provide AI assistants with deep code understanding and relationship analysis. Enables querying code structure, dependencies, and impact analysis through natural language interactions.

Semantic code graph MCP server for coding agents

Enables coding agents to search and retrieve persistent repository memory created by Cliper, providing context, gaps, architecture, and timeline information via MCP tools.

MCP server for Python build123d to help AIs develop and reason about 3D models and CAD

Local-first code intelligence for AI coding assistants: MCP tools, symbol graph search, impact analysis, and auto-index watch for Cursor, Claude Code, and Codex.

A production-packaged Model Context Protocol server for coding agents that routes large file, git, web, database, and other tasks through token-budgeted tools and workflows.

Enables coding agents to navigate and query source code by providing context, symbols, and call graph information through a graph index.

A local .NET MCP server for coding agents, providing persistent semantic indexing, typed-graph DI wiring resolution, reduced context, and compiler-backed verification for .NET projects.

Local-first code intelligence and safety layer for AI coding agents. MCP server exposes dependency graph, impact analysis, and AST-compressed repo context, backed by typed local memory, patch-scope safety gates, and git-independent transaction rollback.

A local vector database system that provides LLM coding agents with fast, efficient semantic search capabilities for software projects via the Message Control Protocol.

An MCP server for codebase context that gives AI coding agents structural understanding through symbol graph, semantic search, blast radius, and convention detection tools.

Enables users to run shell commands with compressed output, extract information from large files, drive local opencode and Codex agents, perform browser-automation tasks, and monitor runs via a live dashboard.

Graph-native bounded working memory for coding agents with typed memories, causal retrieval, current-vs-stale state queries, and compact small-model briefs.

Exposes a codebase's symbol graph and symbol-aware editing tools to AI agents, enabling targeted symbol lookup, impact analysis, and atomic multi-file edits with reduced context tokens.

Maintains an always-correct structural code graph and serves token-budgeted, confidence-labeled context to AI coding agents via 8 consolidated MCP tools.

Provides live, graph-verified code intelligence for AI agents, including call graphs, dependency awareness, and safety gates on edits to prevent breaking changes.

Enables coding agents like Cursor to access Figma design data, fetch layout and styling information, and implement designs in any framework.

https://gopls-mcp.org/ MCP server for golang projects development: Expand AI Code Agent ability boundary to have a semantic understanding and determinisic information for golang projects.