"Cash App" matching MCP servers:

Enables AI assistants to diagnose and fix iOS app issues that cause App Store rejections, such as missing privacy manifests, permission strings, and test credentials, before submission.

An MCP server that reverse-engineers a rebuild spec (CLAUDE.md, config, and mutation-tested tests) from an existing app, enabling coding agents to rebuild the app cleanly against that spec.

Visualize anything from Claude Code as a mermaid diagram in a locally hosted web app.

Gives coding agents source-grounded Apple developer knowledge by searching an index of WWDC20–WWDC26 sessions, Apple documentation, tutorials, Human Interface Guidelines, Swift Evolution, and App Store Review Guidelines. Enables API availability and deprecation lookups, transcript and "what changed" queries, guideline checks, and repo-level Swift app audits through 45 read-only tools.

A Model Context Protocol server that facilitates communication between ABAP systems and MCP clients, providing tools for managing ABAP objects, handling transport requests, and performing code analysis to enhance ABAP development workflows.

Enables AI coding agents to test C++ code by running static analysis, sanitizers (ASan, TSan, LSan, UBSan), and performance profiling through eight MCP tools.

Enables asking a consuming app in plain language to audit its design system usage, returning tables that sort every value and component by origin—design system, app-named, or none—along with token and contrast checks.

Enables Dynamics 365 Finance & Operations developers to search, retrieve, and analyze X++ code across a large indexed codebase, with tools for impact analysis, extensions, references, and best-practice validation.

MCP server for the hy-app component library, enabling AI editors to search components, retrieve APIs and examples, check platform support, access hooks and tools, and validate component usage.

Enables an AI agent to reverse-engineer a compiled program with Ghidra — inspecting decompiled code, instructions, callers, memory bytes, and strings — without any source code. Every reply is labelled as fact, computed, or guess, and each claim traces back to hash-checked saved evidence.

A Python MCP server that decompiles Android APK/XAPK/JAR/AAR files and extracts HTTP APIs used by the app.

Enables powerful code search across millions of public GitHub repositories using grep.app API, with capabilities to discover code examples, retrieve specific files, and batch fetch multiple files for learning from open source projects.

Enables offline Android package analysis by loading APK, DEX, JAR, class, and XAPK files to inspect app metadata, manifests, classes, methods, resources, strings, and cross-references, and to export decompiled Java sources through CRUDE MCP-AQL tools.

Exposes a running VS Code instance to AI agents via MCP, enabling file operations, LSP diagnostics, terminal commands, Git workflows, and refactoring.

Indexes Kotlin/Native platform klibs to enable fast symbol lookup for iOS APIs. Supports querying classes, members, and platform constants via MCP tools for Kotlin Multiplatform development.

Helps LLMs write correct Ghidra scripts by providing API call-flows extracted from Ghidra's source code.

Enables MCP-compatible clients to inspect Next.js codebases, analyze App Router and Pages Router structure, discover API routes, and audit build performance through controlled tools.

Provides AI coding agents with ground truth about a Rails app via 45 read-only tools covering schema, models, routes, controllers, views, jobs, and conventions, working over MCP or CLI and functioning even when the app cannot boot.

Enables MCP clients to work with SAP systems through development-tool APIs for object search, source retrieval and editing, syntax checks, activation, transports, debugging, refactoring, Git operations, and analysis within a single SAP session. Large results can be retrieved through paged reads.

MCP server for angr binary analysis API workflow retrieval. Extracts real API usage patterns from angr's source code and angr-doc examples, then indexes them with semantic search so LLMs can query correct API call sequences.