"The Sounds Resource" matching MCP servers:

Enables AI assistants to query and analyze Rell code via Language Server Protocol features like hover information, completions, diagnostics, and code actions. Automatically manages the Rell LSP server and provides resource-based access with real-time subscriptions.

Provides AI agents with a hardened code security scanner via scan_code and scan_path, detecting leaked secrets and insecure patterns while enforcing strict path confinement and resource bounds.

A Stryker plugin that provides mutation testing results via a local MCP server, enabling AI agents to access the latest mutation report as a JSON resource.

MCP server for interacting with Knights of the Old Republic game installations, enabling resource discovery, inspection, and analysis for AI agents.

Scans a local codebase and exposes file structure, language counts, Python import dependencies, circular dependency detection, framework detection, entry point candidates, and a cached repo structure resource.

Enterprise-grade code intelligence server for AI assistants that analyzes codebases for due diligence, tech debt, security vulnerabilities, and code quality.

An experimental MCP server offering calculator, GitHub repository fetching, a movie database resource, and JavaScript code review capabilities. Designed for learning and compatible with Cursor and Claude Desktop.

Exposes 11 agent-native QA tools over stdio so coding agents can discover a project, analyze risk, select the smallest relevant test set, generate and dry-run tests, triage failures, propose tiered heals, analyze flakes, generate quality reports, and evaluate release readiness. Planning and healing tools stay read-only or propose-only, while execution defaults to dry-run, keeping agent actions safe and evidence-backed.

Turn your typed TypeScript functions into an MCP server — tool, resource, and prompt schemas inferred from your types and JSDoc. No schema library, no decorators, no boilerplate.

Provides AI agents with a unified MCP interface for Android APK reverse engineering, metadata analysis, Smali/resource editing, rebuilding, signing, verification, and ADB device testing.

Enables MCP clients to inspect, edit, and validate Arma Reforger addons in Workbench, including Enforce Script source, prefab/world files, resource GUIDs, and Workbench startup diagnostics.

Enables an agent to import real dependency manifests and source excerpts, resolve each cryptographic surface to an algorithm, and assess it against published quantum resource estimates and NIST transition dates. Supports recording migration decisions with shelf-life inputs, replaying the SHA-384 seal chain, and exporting a cited migration plan with a start-by year for every surface.

An MCP server that detects energy anti-patterns in Python code, retrieves optimization examples, suggests refactoring, validates correctness, and benchmarks resource gains, integrating with VS Code, Cursor, and Windsurf.

Provides AI assistants with progressive code intelligence to explore repository structure, find symbols, and assess the impact of changes using ast-grep.

Enables querying any GitHub, GitLab, or Bitbucket repository and getting AI-generated answers about the codebase, powered by Gemini.

MCP server for AI-assisted development on the Lampa open-source TV app, providing read-only access to the source tree with 41 tools and 5 resources for structured analysis, planning, editing, and validation.

Enables AI coding agents to list, read, create, update, delete, and render PlantUML and Mermaid diagrams, and check them against the actual codebase to detect architecture drift.

Enables asking natural-language questions about any GitHub repository and receiving answers grounded in the actual code, with read-only enforcement and automatic repository fetching.

Copy/paste (duplicate code) detection for codebases. The jscpd detector's built-in MCP server lets AI agents check snippets and files for duplication against a scanned project over stdio (jscpd --mcp).

coldstart gives coding agents durable memory of a specific codebase, with no embeddings, no vector store, and no API key. Agents write notes after real tasks — what a file is for, how a flow spans files, which invariants hold — and those notes are surfaced automatically to a later session whose task matches, so nothing gets rediscovered from scratch. Every note is anchored to real files by content