How to build a Lovable app that lets AI models like GPT and Claude play a simulation game through MCP, from planning to backend integration.
What does it take to build a game that AI models can play through MCP? #
Building a simulation game that language models can actually play, rather than just talk about, requires three things: a working app shell, a persistence layer to store game state, and an MCP (Model Context Protocol) server that exposes a small set of tools an AI can call. In one recent build, a developer used Lovable, an AI-assisted app builder, to construct exactly this: a settlement-survival simulation where models like GPT and Claude could inspect a settlement, check on citizens, and submit daily decisions through an API instead of a chat window. The process moved through distinct phases: planning, visual shell, backend/cloud integration, and MCP wiring, each handled as a separate step rather than one giant prompt.
TL;DR #
Phased prompting worked better than asking Lovable to build everything at once. The build was split into a plan mode step, a basic visual shell, cloud/database integration, and then MCP wiring, each with its own prompt.Plan mode acted as insurance. Before any code got written, the model producing the plan (GPT 5.6 in this case) generated an overview with explicit acceptance criteria, so the builder could check Lovable’s output against a checklist instead of guessing whether it succeeded.The MCP surface was kept deliberately small. The exposed tools let a connected AI inspect the settlement, inspect citizens, submit a daily decision, and read the final report, four core actions rather than a sprawling toolset.Lovable’s architecture separated concerns cleanly, with a browser-facing game layer, a Lovable Cloud backend for user data and run history, and scenario templates that could be reused across different playthroughs.Visual polish came last and took the longest. Days of manual play testing and by-hand fixes in Lovable followed the automated build phases, since the initial UI was intentionally basic.Custom skills could be imported into Lovable’s settings, giving the builder a way to bake project-specific instructions into the tool ahead of later build phases.** The finished game let multiple LLMs run the same scenario independently**, each with its own run ID, same seed, and same starting resources, which is what made a fair head-to-head comparison possible in the first place.
Other agents start typing. Remy starts asking. #
Scoping, trade-offs, edge cases — the real work. Before a line of code.
Why build the game in Lovable instead of coding it from scratch? #
Lovable is a no-code/low-code AI app builder that turns natural-language prompts into working web applications, including backend infrastructure through its “Lovable Cloud” offering. For a project like this, the appeal is speed: instead of hand-writing a database schema, an API layer, and a frontend, the builder describes what’s needed and lets the tool generate it, then iterates.
The tradeoff is control. Because Lovable interprets prompts and makes its own architectural decisions, the plan mode step becomes important. Rather than jumping straight into code generation, the builder first had an external model (GPT 5.6) draft a full project plan, including a folder structure, database design, backend engine description, and a list of acceptance criteria. That plan was pasted into Lovable’s plan mode, which let the builder confirm Lovable understood the goal before any files were touched. If Lovable’s summary didn’t match the listed criteria, that was the signal something had gone wrong early, before wasted build cycles compounded the problem.
How was the MCP layer designed? #
MCP (Model Context Protocol) is what let outside language models actually play the game instead of just describing what they’d do. Rather than exposing dozens of granular functions, the build settled on a small, specific set of tools: inspect the settlement, inspect individual citizens, submit a daily decision, and read the final report once a run ended.
Keeping this tool count low was a deliberate choice. A minimal API surface reduces the chance of a model calling the wrong tool or getting confused by overlapping options, while still giving it everything it needs to reason about the game state and act. All of a model’s choices for a given day, rations, repairs, expeditions, medicine allocation, funneled into a single “submit decision” call. That simplicity also made it possible to run controlled comparisons: two different models could be given the same seed, the same starting resources, and the same rules, then have their decisions logged independently by run ID.
What did the actual build sequence look like? #
The build moved in phases rather than a single prompt-and-done pass:
Phase one covered the basic visual shell, a one-day demo version of the game with simple graphics, letting the builder click into individual citizens to see stats like morale, health, and fatigue. Cloud integration, authentication, and NPC logic were explicitly deferred so this phase could stay focused and verifiable.
Phase two expanded the simulation logic and fleshed out gameplay across the full seven-day arc.
Phase three added backend persistence, connecting the app to Lovable Cloud so that runs could be saved, retrieved, and compared later.
After that came the MCP contract itself, a markdown file describing the tool interface Lovable would need to expose, which the builder noted seemed to lean on Claude under the hood as part of Lovable’s own generation process.
Remy is new. The platform isn't. #
Remy is the latest expression of years of platform work. Not a hastily wrapped LLM.
Only after the backend and MCP layer were functional did visual polish get addressed, and that stage took noticeably longer than the automated phases, involving manual highlighting of UI elements in Lovable and repeated play testing to catch bugs or awkward interactions before the game was considered final.
Is this approach worth using for other AI-agent projects? #
For anyone wanting to test how language models behave under constrained, resource-scarce decision-making, this kind of build offers a repeatable framework: a defined scenario, a fixed seed, identical starting conditions, and a narrow API that forces the model to act through structured tool calls rather than free-form chat. That structure is what made it possible to compare two flagship models, in this case GPT 5.6 and Claude Opus 5, managing the same settlement crisis and see materially different outcomes in survivor counts, resource management, and ethical tradeoffs. The phased build process (plan, shell, backend, MCP, polish) is also broadly reusable outside of game projects. Any scenario where you want an AI to interact with a stateful system, rather than just answer questions about it, benefits from the same pattern: a small, well-documented set of tools, explicit acceptance criteria before building, and incremental verification at each phase rather than a single monolithic build attempt.
The main cost is time. Getting from a rough concept to a polished, MCP-connected simulation involved multiple build phases and, by the builder’s own account, several days of manual refinement and testing after the automated phases wrapped up.
Frequently Asked Questions #
What is MCP and why does it matter for AI-controlled games?
MCP (Model Context Protocol) is a standard way for language models to call external tools and access structured data. For a game, it means an AI can query game state and submit actions programmatically, letting it actually play rather than just describe hypothetical moves in a chat interface.
What is Lovable used for in this kind of project?
Lovable is an AI-assisted app builder that generates web applications, including frontend UI and backend infrastructure via Lovable Cloud, from natural-language prompts. It was used here to build both the visual game shell and the backend systems needed to persist game runs and expose an MCP interface.
Why did the builder use a “plan mode” step before building anything?
Plan mode let the builder confirm that Lovable understood the full scope of the project, including acceptance criteria, before any code was generated. This served as a checkpoint to catch misunderstandings early rather than discovering them after a build phase was already complete.
How many tools did the MCP server expose?
The build kept the MCP surface intentionally small: tools to inspect the settlement, inspect citizens, submit a daily decision, and read the final report. This minimal design reduced ambiguity for the connected AI models while still covering everything needed to play a full run.
Can this same approach be used for projects beyond games?
Yes. The core pattern, a small MCP tool set, phased builds with acceptance criteria, and a persistence layer, applies to any project where you want an AI to interact with a live system rather than just generate text about it.