"Flutter and Dart source code repositories" matching MCP servers:

A real-time server that provides Flutter/Dart documentation and pub.dev package information to AI assistants, ensuring they generate accurate and up-to-date Flutter code.

Enables AI coding assistants to search, retrieve, and reuse validated Flutter animations with documented pitfalls, directly integrable into Claude Code or Cursor.

Integrates the Pub.dev API with AI assistants to provide real-time Flutter package information, documentation, and trend analysis. It enables users to search for packages, compare versions, and evaluate quality scores through natural language commands.

An MCP server that enables AI assistants to navigate, search, and analyze local Keycloak source code to support developer customizations like SPIs and authenticators. It provides tools for searching classes, generating boilerplate code, detecting breaking changes between versions, and tracing dependencies.

Provides on-demand access to AOSP and Jetpack source code to help AI understand Android framework internals. It features a hybrid architecture using Tree-sitter and LSP for precise code extraction, cross-file navigation, and local source indexing.

Local-first MCP server providing deterministic Flutter/Dart static analysis, knowledge search, and project review with structured insights and confidence-based findings over an indexed SQLite store of official repositories.

Provides AI agents with a governed, three-layer project memory (guide, code facts, and knowledge) through namespaced MCP tools for code search, context compilation, impact analysis, and proposal-driven documentation updates.

Allows LLM tools like Claude Desktop and Cursor AI to access and summarize code files through a Model Context Protocol server, providing structured access to codebase content without manual copying.

Searches Claude Code documentation via BM25 indexing, returning ranked snippets with category filtering. Enables fast, local search over official docs without full-document scans.

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.

Connects to vibe-code-best-practices.vercel.app to index 817 tools across 185 sections and 10 domains, routes the right tool/Skill for your project need, and can recommend a full stack.

Facilitates searching and accessing programming resources across platforms like Stack Overflow, MDN, GitHub, npm, and PyPI, aiding LLMs in finding code examples and documentation.

Enables AI assistants to search and query documentation from multiple sources including Voiceflow and Claude Code, with full-text search, code examples retrieval, and step-by-step tutorials access.

An MCP server that provides static validation and documentation lookup for quantum computing libraries, currently supporting PennyLane. It allows users to verify quantum code syntax and method usage against official library documentation to ensure correct implementation.

Enables Claude to access and search UVM 1.2 source code, class hierarchies, and method implementations, providing expert-level knowledge of the Universal Verification Methodology.

An MCP server that performs code reviews by comparing local source code against design documents stored in Confluence. It integrates with Atlassian MCP servers to analyze documentation and provide suggestions for quality improvement based on design specifications.

Analyzes source code across multiple languages to extract structural elements like classes, functions, and parameters using tree-sitter. It provides LLM-optimized markdown output that includes nesting levels, line numbers, and signatures to facilitate codebase navigation.

Provides AI assistants with access to personal best practice skills and coding guidelines stored as markdown files in a GitHub repository, enabling retrieval and listing of custom development standards.

Enables document-driven development by automatically detecting Markdown spec changes, generating implementation plans, and syncing code and tests through MCP tools.

A Model Context Protocol server for querying and reading articles from a how-to documentation portal, with search and retrieval tools scoped to the token's user permissions.