"Proof-of-Concept Examples for Multi-Component Processes (Model Context Protocol)" matching MCP servers:

A Python-based server for managing and analyzing code context for AI-assisted development. Provides real-time code analysis, project structure analysis, dependency tracking, and secure API endpoints.

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.

Enables AI agents to understand, inspect, create, validate, build, and document Jekyll component frameworks through Jekyll-aware tools, resources, prompts, sandboxed filesystem access, and configurable write modes.

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.

Enables querying Odoo's model registry (fields, inheritance, methods) without a database connection by loading the registry in-memory.

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

Enables AI coding agents to automatically compile the most relevant slice of a repository into context for a given task.

Provides AI agents with secure, read-only file system access to analyze and understand project codebases, enabling multi-repository context aggregation and cross-project code tracing.

An intelligent MCP server that acts as an AI tech lead for coding agents, providing expert validation, impact analysis, and strategic guidance before code changes are made.

Enables coding agents to query a local, versioned knowledge graph of a software project, retrieving overviews, context packs, evidence, and explanations to make informed changes.

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.

MCP server that helps AI coding agents understand a repository by providing lightweight tree/map, code search, and token-budgeted context packing tools without dumping the entire monorepo into the prompt.

An MCP server that provides dynamic codebase context to Claude Code through tools like hybrid search, recent changes, and symbol definitions, enhancing AI-assisted coding with local RAG.

A self-contained, in-memory graph database for AI Agents. Provides semantic code understanding through the Model Context Protocol (MCP).

Enables AI-driven semantic code search by leveraging Windsurf's reverse-engineered protocol to perform multi-round local searches using natural language. It automatically executes bundled ripgrep and file operations to return relevant code snippets and file paths to MCP-compatible clients.