"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.

Provides AI coding agents with five intelligence layers (dependency graph, git history, documentation, architectural decisions, code health) via nine MCP tools, enabling deep codebase understanding and reducing exploration cost.

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

Shared knowledge graph for AI coding agents. Search, create, and link verified technical knowledge across 12 node types with trust scoring, demand signals, and auto-provisioning. Install: npx agent-hive-mcp

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.

MCP server for the AIMFP paradigm, enabling AI assistants to manage functional programming projects with persistent database state, directive compliance, and task tracking via JSON-RPC tools.

Gives LLM coding agents a live code graph alongside their own tools, enabling ranked repo maps, fused lexical and graph search, symbol callers/callees and impact analysis, architecture summaries, and previewed graph-addressed edits. Adapters can extend it with custom nodes, queries, and tools.

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.

Connect an AI assistant to aCelery, describe the app you want in plain words, and it writes the app, creates its database, runs it on your phone, checks for errors and fixes them — while you watch.

Enables coding agents to query a parsed graph of a repository and to recall what earlier sessions learned about it, both stored in FalkorDB behind one traversal. It exposes eight MCP tools over stdio for locating symbols, tracing calls and impact paths, and writing or recalling user- and repo-scoped facts, procedures, and handoffs.

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.

Enables MCP-compatible AI coding tools to store and recall persistent memories across sessions and projects, with keyword and semantic search backed by a local SQLite database.

Provides AI coding agents with access to Figma design data through the Figma API. Enables accurate one-shot implementation of designs in any framework by translating Figma files into simplified layout and styling information.

Enables autonomous coding agents to perform deterministic pre-flight inspection of large legacy repositories: pruning vendored, generated, minified, binary, and symlinked bloat; quarantining hardcoded secrets; detecting circular dependency knots; and partitioning the codebase into token-bounded, bottom-up wave-scheduled work slices. It also exports the dependency graph as Neo4j Cypher and accepts caller-supplied edges for dynamic or reflective dependencies.

Enables coding agents to query and commit to a research graph that remembers failed experiments, ensuring reproducibility and preventing redundant work.

MCP server of Larvyn, a desktop workbench for Laravel apps. Gives coding agents tools for the project's context, terminals, dev services, Artisan and tests, models and database, ports and other agents, plus Laravel playbooks as prompts.

Enables Cursor and other MCP clients to launch OpenCode V2 agents with selected models, manage and continue sessions, cancel tasks, and inspect agent/model/status information through MCP tools.

Local-first MCP server that indexes all projects on your machine and provides coding agents with briefings about each project's stack, services, and run instructions, so they can start sessions with context across all repos. No cloud, no accounts, no telemetry.

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.

Enables autonomous software-engineering tasks in a repository by coordinating filesystem, GitHub, database, and CI/CD tools through MCP, with a deterministic safety boundary for permissions, validation, timeouts, duplicate protection, and test verification.

Enables AI coding agents to access and update a shared local database for tracking pull requests and work items across repositories.

Provides persistent long-term memory for AI coding agents by storing entities, relations, and observations across different sessions. It enables users to manage and query structured knowledge like coding preferences, project patterns, and technical solutions via a graph-based storage system.

MCP server to provide Jira Tickets information to AI coding agents like Cursor

Bridges Large Language Models with Language Server Protocol interfaces, allowing LLMs to access LSP's hover information, completions, diagnostics, and code actions for improved code suggestions.