"Node-RED" matching MCP servers:

A WorkFlowy MCP Server with node creation, recursive gets, updates, completions, search and replace (with a dry run mode), usage reports, a configurable list of exposed commands and customizable log output.

Enables coding agents to develop on one machine and verify results on another, using branch-bound runbooks, isolated checkpoints, and structured receipts to guide repair iterations.

RegressGuard is an MCP server that records a known-good baseline and lets AI coding agents detect regressions (broken API contracts, failing tests, schema changes) during their edit loop, allowing them to self-correct before committing.

Browser automation for AI agents via the Model Context Protocol, enabling web navigation, form filling, screenshots, and more using Chromium.

An MCP server to create secure code sandbox environment for executing code within Docker containers.

Discover and communicate with AI agents over encrypted P2P networks. Zero-config NAT traversal, skill-based routing, and end-to-end encryption.

A local MCP server that indexes codebases into Neo4j and provides tools for code analysis, dependency visualization, and impact assessment.

MCP server that provides AI coding assistants with access to call graphs, data flows, and security analysis for multi-file vulnerability detection.

An MCP server that provides persistent memory for AI agents, enabling context retention across sessions and supporting vibe-loop workflows where agent promises and deliverables are tracked and validated.

Enables AI assistants and automation agents to query Talos Linux clusters and nodes for versions, events, service and kernel logs, resource usage, and discovery members through the Talos API, with read-only tools by default and optional node reboot when explicitly allowed.

Turns a codebase into a queryable graph with semantic search, call graphs, and control/data flow analysis, served to AI coding agents via the Model Context Protocol.

Enables reading and searching API documentation from YApi instances, allowing AI models to access interface definitions, project API lists, and search through API endpoints using YApi URLs or project IDs.

Enables signed-in users and organizations to discover and execute read or owner-confirmed write operations for monitoring sites, TLS certificates, traffic, suspicious requests, HTTP errors, Docker/Podman workloads, storage, data services, jobs, and node health. It connects through OAuth authorization code with PKCE and supports incident and TLS review prompts.

AI modifies a function but misses 5 callers that also need updating. crag fixes this — it builds precise call graphs via static analysis, so AI knows exactly what's affected before making changes.