"Proof-of-concept examples for Multi-Component Processes (MCPs)" matching MCP servers:

Enables searching for component, function, and type usage in React/TypeScript projects.

Provides AI assistants with access to private React component library documentation, props, and code examples through type-safe TypeScript integration.

An MCP server that exposes a Svelte/SvelteKit project's component dependency graph over stdio, enabling AI coding assistants to query component imports, unused components, and route dependencies without an editor.

MCP server that provides six verification tools (Lean proof checking, axiom audit, bound, gridlock check, certificate verification, residency check) with honest status reporting (ok/failed/unavailable) to prevent agents from claiming unchecked proofs passed.

An MCP server that scans React and Vue projects, extracts component metadata (props, slots, events, imports, usage), and exposes it to AI coding agents via structured tools.

Enables AI to precisely use local Vue components by extracting their Props, Events, and Slots via AST analysis.

Paid remote MCP server for ShipSwift providing tools to search SwiftUI components, generate patches, validate dependency policies, and export component receipts.

Harvests React components from Aceternity UI and Shadcn UI, fetching code and metadata to generate integration prompts for AI agents.

Automatically extracts technical concepts from AI coding conversations, organizes them into a searchable knowledge base with hierarchy and categories, and links them to specific locations in your codebase.

Enables AI assistants and developers to analyze code for language-specific best practices and idiomatic patterns across programming languages, CI automation, and configuration formats.

Exposes CodeGraph capabilities for multiple local repositories to MCP-compatible AI coding assistants, enabling multi-repo code exploration and cross-repo symbol tracing.

A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Extends IDA Pro with multi-instance support and vulnerability scanning for AI-assisted reverse engineering.

Orchestrates multiple LLM agents to perform parallel code review, then synthesizes and votes on suggestions to rank improvements by consensus.

Enables dynamic exploration of Angular library components by reading and analyzing the public API exports. Provides tools to list available components and retrieve detailed component information.

Runs a multi-agent code review pipeline via MCP, analyzing git diffs for security, quality, and logic issues and returning a pass/fail verdict.

Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.

A multi-model AI orchestration MCP server for automated code review and LLM-powered analysis, integrating with Claude Code and OpenCode to orchestrate multiple AI models for code quality checks, security analysis, and multi-agent consensus.

Enables AI agents to query component governance rules, validate component props, and generate development prompts for questionnaire editors.