"A tool for decompiling Android APK files" matching MCP servers:

Enables LibreChat agents to manage per-user private files, persistent documentation, RAG indexing and semantic search, and publish files via secure opaque public links.

A tool discovery MCP server that integrates with Azure DevOps wikis and code-graph-rag to match natural language goals to tools, enabling tool composition and proxy testing.

Provides Android development assistance following NowInAndroid best practices, including architecture patterns, code generation for feature modules/ViewModels/repositories, and searchable documentation for Jetpack Compose and modularization.

An MCP server that gives AI agents direct access to developer.android.com — search docs, look up API references, browse packages, and check release notes.

Provides AI coding assistants with access to Google's Android development skills library through an MCP server. Enables developers to discover, search, and apply Android best practices directly within their coding environment without manual copying.

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.

Automatically crawls documentation websites, converts them to organized markdown files, and generates condensed cheat sheets. Intelligently categorizes content into tools/APIs and provides local-first access to downloaded documentation.

A Bun-based MCP server that allows AI models to query Swagger/OpenAPI documentation from local files or remote URLs. It enables users to search for APIs, retrieve detailed endpoint definitions, and fetch schemas to facilitate code generation and API integration.

An MCP server that helps transfer C4 diagrams from DrawIO and PlantUML into a DocHub architecture repository.

Provides AI assistants with specialized tools to interact with NIST's Open Security Controls Assessment Language (OSCAL) framework. It enables agents to retrieve schemas, explore models, and generate valid OSCAL documentation for security compliance automation.

Optimizes EDT MCP tool descriptions by replacing verbose documentation with concise summaries stored in SQLite, reducing context usage while allowing on-demand retrieval of full tool guides.

Provides controlled, verifiable access to jw.org content for AI applications, enabling search, article retrieval, and scripture lookup while preventing hallucinations.

Provides calculator tools for mathematical operations, access to TypeScript SDK documentation, and meeting summary prompt templates through both stdio and SSE transports.

Enables parsing and extraction of content from various document formats (PDF, Word, Excel, PowerPoint) into Markdown format using the Niutrans document API.

Provides calculator tools for mathematical operations, document resources for accessing TypeScript SDK documentation, and prompt templates for generating structured meeting summaries. Built with FastMCP to demonstrate comprehensive MCP capabilities including tools, resources, and prompts in a single implementation.

Enables querying documents through a Langflow backend using natural language questions, providing an interface to interact with Langflow document Q\&A flows.

A tool that helps users conduct comprehensive research on complex topics by exploring questions in depth, finding relevant sources, and generating structured, well-cited research reports.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.

Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.

Provides semantic search capabilities over PyTorch documentation, enabling users to find relevant documentation, APIs, code examples, and error messages through Claude Code integration.