"A tool for modifying game memory and values" matching MCP servers:

Bridges AI assistants to running game instances for testing, debugging, and verification. Works with any game engine via a simple TCP protocol.

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

MCP server for running deterministic Godot game scenarios, listing available scenarios, running single or soak tests, and reading test reports.

Enables AI agents to create, run, and debug 2D games in a sandboxed engine, providing tools for simulation, state inspection, and quality validation.

Enables AI clients to validate game level progression data, audit localization packs for missing translations and dynamic-token mismatches, and run deterministic evaluations of generated puzzle or notification content.

Audits MCP tool descriptions for quality and reliability, scoring them 0-100, detecting smells, and providing rewritten versions for better agent accuracy.

An MCP server connecting Claude AI with game engine runtimes (Unity & Godot) for automated testing, log filtering, and scene hierarchy inspection.

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.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

Protects AI agents from bad tool outputs, schema mismatches, and tool poisoning by validating, sanitizing, and scoring reliability of tool responses.

Provides deterministic, offline, sourced resolutions for 44 mobile game development problems spanning performance, memory, rendering, store policies, IAP billing, crash vitals, live-ops emergencies, and store-policy deadlines, along with pre-launch checklists and launch-day runbooks. It also statically audits project directories for Google Play and App Store compliance, including target SDK, 16 KB ELF alignment, privacy manifests, and secret-file exposure, without any network calls or shell execution.

A lightweight multi-provider MCP testing fixture with two providers (eva_air and uk_railway), each exposing 5 tools using in-memory fake data, designed for verifying MCP client tool discovery, tool calls, and multi-provider routing.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Enables verification of deployment-scoped model-visible DSH tool surface conformance by checking recorded tool identities and schemas against approved locks, reporting only drift categories and hashes.

Analyzes multi-step AI agent tool chains to compute success probability, identify bottlenecks, and suggest better execution orders, enabling more reliable agents via local pure-math computation.

This MCP server verifies that an agent’s claimed tool output matches the actual response returned by a tool to prevent invented or distorted results. It flags mismatches, omissions, and invented fields to ensure downstream logic only processes verified tool data.

Enables Playwright QA automation with a custom Memory MCP that provides tiered retention and mortgage compliance audit, allowing AI agents to remember flakiness history and journey maps while adhering to data privacy and audit requirements.

Persistent visual cache for LLM-driven software development. Caches screenshots using perceptual hashing, vector search, and AX trees to prevent token overhead and visual hallucination loops.

MCP-first A/B testing server that enables agents to manage experiments (create, update traffic splits, read results, apply winning variants) from tools like Claude Code, with self-hosted Cloudflare backend.

MITM proxy MCP server that intercepts and verifies AI agent tool calls, detecting fabrication, tracking costs, verifying outcomes, and inferring user satisfaction.