Flatland — Financial models as code.

# Flatland > Flatland is a financial modeling MCP server and HTTP API. AI agents build, compile, > and analyze structured financial models as typed computation graphs. Users describe > their business to their AI agent (Claude Code, Windsurf, or any MCP client); the > agent calls Flatland's tools; Flatland returns compiled, typed, auditable financial > output. The agent generates Excel exports with live formulas using openpyxl. ## Product - [Flatland](https://flatlandfi.com): Main product page with overview, pricing, and quickstart - [Setup](https://flatlandfi.com): Subscribe and get an API key. Then run: `npx flatland-setup <api-key>` ## Pricing $35/month. 14-day free trial. Single tier. Subscribe: https://flatlandfi.com ## Core Concepts Flatland's primary abstraction is the **driver** — a named, typed, dependency-aware node representing one business assumption or computed relationship. Drivers form a directed acyclic graph (DAG). The IR (Intermediate Representation) is the typed DAG: the stable contract between the AI layer and the deterministic computation engine. Driver types: Currency, Percentage, Ratio, Count, Duration, Rate (and open-ended custom types). The compilation pipeline runs in three passes: type checking → dependency resolution → evaluation. Same IR always produces the same outputs. The AI layer is non-deterministic; the engine layer is deterministic. They are separated by the IR. Scenarios are sparse overlays — a set of driver overrides applied to the shared base graph. Creating a scenario is instant and memory-efficient. Comparing scenarios attributes output deltas to specific changed drivers. ## MCP Tools All tools are available after running `flatland_init`, which loads the skills library into the agent's context. Always call `flatland_init` first in every session. ### Session & model management - `flatland_init`: Load skills, templates, and usage guidance. Call this first every session. - `flatland_create_model`: Create a ne