https://github.com/aklos/scryer
Visual modeling surface for working with AI agents via built-in MCP server
https://github.com/aklos/scryer
ai-tools c4-model claude-code codex diagrams fair-source model-driven-development tauri
Last synced: 5 days ago
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Visual modeling surface for working with AI agents via built-in MCP server
- Host: GitHub
- URL: https://github.com/aklos/scryer
- Owner: aklos
- License: other
- Created: 2025-01-30T12:13:58.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2026-02-28T10:53:37.000Z (15 days ago)
- Last Synced: 2026-02-28T14:45:36.961Z (15 days ago)
- Topics: ai-tools, c4-model, claude-code, codex, diagrams, fair-source, model-driven-development, tauri
- Language: TypeScript
- Homepage:
- Size: 524 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
scryer
Visual architecture models that AI agents build from. Edit C4 diagrams in a drag-and-drop editor — AI agents read, modify, and implement the same model through MCP.
Features
•
Getting started
•
MCP server
•
Building from source
AI agents write code, but natural language is a lossy way to tell them what to build. Scryer gives you a shared visual model — you edit it in a drag-and-drop editor, the agent reads and modifies it through MCP. Once the model looks right, the agent generates code from it — `get_task` feeds work one unit at a time with dependency ordering, inherited contracts, and progress tracking.
Opinionated [C4](https://c4model.com/) hierarchy (system, container, component, operation), typed relationships, behavioral flows, contracts.
## Features
- **C4 Architecture Diagrams** — drag-and-drop editor for persons, systems, containers, components, and operations. Drill down through levels.
- **Behavioral Flows** — model user journeys, data pipelines, deploy sequences. Link flow steps to components.
- **Data Models** — define typed properties on model nodes, visible on the canvas alongside your architecture.
- **Contracts** — always/ask/never rules that constrain how AI agents implement your code. Inherited down the hierarchy.
- **Source Mapping** — link architecture nodes to files in your codebase.
- **MCP Server** — AI agents connect to read, modify, and implement from your architecture model in real-time.
- **AI Advisor** — optional LLM-powered review that flags structural issues in your diagrams.
- **State Machine Workflow** — `get_task` feeds work to AI agents one unit at a time with dependency ordering, contract inheritance, and progress tracking. Build, mark done, repeat.
## Getting started
Download the latest release for your platform from the [releases page](https://github.com/aklos/scryer/releases).
### Typical workflow
1. Tell your AI agent: *"Use scryer to model this project's architecture"*
2. The AI calls MCP tools — nodes appear in the visual editor in real-time
3. Review, drag things around, rename, remove, restructure
4. Tell the AI: *"Implement this model"*
5. The AI reads the model and generates code from it
## MCP server
The MCP server lets AI agents read and modify your architecture models. It ships bundled with the desktop app.
### Setup
In any project directory, run:
```bash
scryer-mcp init
```
This detects which AI tools you have installed (Claude Code, Codex) and writes project-scoped config for each:
- **Claude Code** — `.mcp.json`
- **Codex** — `.codex/config.toml`
Existing config files are preserved — only the `scryer` entry is added or updated. If neither tool is found in PATH, the command tells you.
### Manual setup
If you prefer to configure MCP manually, add scryer to your project config:
**Claude Code** (`.mcp.json` in project root):
```json
{
"mcpServers": {
"scryer": {
"type": "stdio",
"command": "/path/to/scryer-mcp"
}
}
}
```
**Codex** (`.codex/config.toml` in project root):
```toml
[mcp_servers.scryer]
command = "/path/to/scryer-mcp"
```
### What the MCP server provides
- Read the full model or scoped subtrees (`get_model`, `get_node`)
- Add, update, and remove nodes and edges
- Define behavioral flows (`set_flows`)
- Get implementation tasks in dependency order (`get_task`)
- Detect what changed since last read (`get_changes`)
## Tech
Scryer is a [Tauri](https://tauri.app/) desktop app. The UI is written in [React](https://react.dev/) with [TypeScript](https://www.typescriptlang.org/) and the backend is written in [Rust](https://www.rust-lang.org/). Canvas rendering uses [ReactFlow](https://reactflow.dev/) with [d3-force](https://d3js.org/d3-force) for auto-layout.
## Building from source
### Prerequisites
- [Rust](https://rustup.rs/) (stable)
- [Node.js](https://nodejs.org/) 20+
- [pnpm](https://pnpm.io/)
- System dependencies for [Tauri 2](https://v2.tauri.app/start/prerequisites/)
If you use Nix, `shell.nix` provides everything:
```bash
nix-shell
```
### Build & develop
```bash
pnpm install # Install dependencies
pnpm tauri dev # Run full app (Tauri + Vite on :1420)
pnpm dev # Run frontend only
pnpm tauri build # Production build
```
## License
Scryer is [Fair Source](https://fair.io/) software under the [Functional Source License (FSL-1.1-MIT)](LICENSE). You can use it, view the source, and contribute. You just can't build a competitor with it. The license converts to MIT after two years.