"A search for blockchain-related modular component protocols (MCPs)" matching MCP servers:

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

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

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.

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.

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

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

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

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

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.

Enables semantic code search across indexed code folders using vector embeddings, with support for multiple embedding providers and automatic file watching. Provides an admin UI and integrates with MCP clients for natural language code queries.

Semantic + lexical code search as an MCP server. Agents query in natural language and get back ranked file:line ranges to read precisely.

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.

Enables blazingly fast file and content searching in large codebases using ripgrep, with intelligent filtering, fuzzy finding, and directory tree visualization while respecting .gitignore and avoiding common bloat directories.

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.

Clones a Git repo into a temp directory so MCP clients can search and read files from another repo for context.

Enables AI agents to perform semantic code search across entire codebases using natural language queries. Provides fast indexing and ranked search results with line numbers and file paths through the Seroost search engine.

Enables AI coding assistants to semantically search codebases by meaning rather than exact text, with zero external daemons and fully local embeddings. Works out-of-the-box with Claude Code, Gemini CLI, Antigravity, and Cursor.

High-performance code understanding toolkit that enables batch reading of multiple files with dependency context, structural outline extraction with Java annotation awareness, and precise location of classes/methods across large codebases.

Provides semantic code search over codebases using local embeddings with natural language queries. Supports hybrid search, file watching, and respects .gitignore.