docs(mcp): add README, examples, and changelog
- README.md: install/quick start; configs for Claude Desktop, Claude Code, and Cursor; programmatic usage; tables covering all 21 tools, 4 resources, and 3 prompts; limitations; development commands. - CHANGELOG.md: 0.1.0 entry in Keep a Changelog format. - examples/generate-apartment.md: prose transcript using from_brief to build an 80 m² 2-bed apartment, showing apply_patch, set_zone, cut_opening, validate_scene. - examples/renovate-from-photos.md: prose transcript using the vision tools + renovation_from_photos prompt. - examples/embed-in-agent.ts: compilable TypeScript showing programmatic usage via InMemoryTransport. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Adrian Perez
co-authored by
Claude Opus 4.7
Claude Opus 4.7
parent
441e97b2b6
commit
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/**
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* Programmatic `@pascal-app/mcp` usage.
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*
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* Runs a full MCP client/server pair over the in-memory transport inside a
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* single Node process. Useful for agent frameworks and tests that want to
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* drive Pascal without spawning a subprocess.
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*
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* Compile with the package's `tsc --build`, or run directly with Bun:
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*
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* bun run packages/mcp/examples/embed-in-agent.ts
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*/
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import { Client } from '@modelcontextprotocol/sdk/client/index.js'
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import { InMemoryTransport } from '@modelcontextprotocol/sdk/inMemory.js'
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import { createPascalMcpServer, SceneBridge } from '@pascal-app/mcp'
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async function main(): Promise<void> {
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// 1. Spin up the headless bridge. `loadDefault()` seeds a Site → Building →
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// Level stack so the client has something to query immediately.
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const bridge = new SceneBridge()
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bridge.loadDefault()
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const server = createPascalMcpServer({ bridge })
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// 2. Link the server to an in-memory client. Exactly the same API surface
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// as the stdio / HTTP transports, but without any process boundary.
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const [srvT, cliT] = InMemoryTransport.createLinkedPair()
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const client = new Client({ name: 'my-agent', version: '0.1.0' })
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await Promise.all([server.connect(srvT), client.connect(cliT)])
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// 3. Discover available capabilities.
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const tools = await client.listTools()
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console.log(
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'available tools:',
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tools.tools.map((t) => t.name),
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)
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// 4. Inspect the current scene.
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const scene = await client.callTool({ name: 'get_scene', arguments: {} })
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console.log('scene snapshot:', JSON.stringify(scene, null, 2))
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// 5. Find the default level, create a 5 m wall, and undo it.
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const levels = await client.callTool({
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name: 'find_nodes',
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arguments: { type: 'level' },
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})
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const levelId = (levels.structuredContent as { nodes: Array<{ id: string }> }).nodes[0]?.id
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if (levelId) {
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const created = await client.callTool({
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name: 'create_wall',
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arguments: {
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levelId,
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start: [0, 0],
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end: [5, 0],
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thickness: 0.2,
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height: 2.5,
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},
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})
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console.log('created wall:', created.structuredContent)
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const undone = await client.callTool({ name: 'undo', arguments: { steps: 1 } })
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console.log('undone:', undone.structuredContent)
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}
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// 6. Validate and export.
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const validation = await client.callTool({ name: 'validate_scene', arguments: {} })
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console.log('validation:', validation.structuredContent)
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const exported = await client.callTool({
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name: 'export_json',
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arguments: { pretty: true },
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})
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console.log('export size:', (exported.structuredContent as { json: string }).json.length)
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await client.close()
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await server.close()
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}
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main().catch((err) => {
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console.error(err)
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process.exit(1)
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})
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# Generate a 2-bed apartment from a brief
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This example walks through a realistic session with an MCP host (Claude
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Desktop, Claude Code, or Cursor) that has `pascal-mcp` configured. The agent
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uses the `from_brief` prompt to turn a short brief into a concrete scene.
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## The brief
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> **User:** Claude, create a 2-bedroom 1-bath apartment in 80 m² in Spain.
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The host UI lets the user select the **`from_brief`** prompt and fills in:
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```text
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brief: "2-bedroom 1-bath apartment in 80 m² in Spain, open-plan living /
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kitchen, bathroom on the interior wall"
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constraints: "Spanish building regulations; ceiling height 2.5 m"
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```
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## What the agent does
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The prompt returns a system message instructing the agent to start from an
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empty site, read the current scene, and emit incremental `apply_patch` calls.
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The agent proceeds roughly like this:
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### 1. Inspect the current scene
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```jsonc
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// tool: get_scene
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{ "name": "get_scene", "arguments": {} }
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```
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Response (trimmed):
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```jsonc
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{
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"nodes": {
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"site-1": { "type": "site", "id": "site-1", "children": [/* ... */] },
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"building-1": { "type": "building", "id": "building-1", "parentId": "site-1" },
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"level-1": { "type": "level", "id": "level-1", "parentId": "building-1",
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"elevation": 0, "height": 2.5 }
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},
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"rootNodeIds": ["site-1"]
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}
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```
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The default scene is a Site → Building → Level stack with no walls. The
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agent decides to work on `level-1` and targets a 10 m × 8 m = 80 m² outline.
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### 2. Create the perimeter walls
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The agent chooses a rectangular outline with its origin at (0, 0):
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```jsonc
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// tool: apply_patch
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{
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"name": "apply_patch",
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"arguments": {
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"patches": [
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{ "op": "create", "parentId": "level-1",
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"node": { "type": "wall", "start": [0, 0], "end": [10, 0],
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"thickness": 0.2, "height": 2.5 } },
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{ "op": "create", "parentId": "level-1",
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"node": { "type": "wall", "start": [10, 0], "end": [10, 8],
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"thickness": 0.2, "height": 2.5 } },
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{ "op": "create", "parentId": "level-1",
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"node": { "type": "wall", "start": [10, 8], "end": [0, 8],
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"thickness": 0.2, "height": 2.5 } },
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{ "op": "create", "parentId": "level-1",
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"node": { "type": "wall", "start": [0, 8], "end": [0, 0],
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"thickness": 0.2, "height": 2.5 } }
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]
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}
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}
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```
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Response:
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```jsonc
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{ "applied": 4 }
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```
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### 3. Create interior partitions
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Two bedrooms on the east side, bathroom on the interior wall, open-plan
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living / kitchen on the west.
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```jsonc
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// tool: apply_patch
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{
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"name": "apply_patch",
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"arguments": {
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"patches": [
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{ "op": "create", "parentId": "level-1",
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"node": { "type": "wall", "start": [5.5, 0], "end": [5.5, 8],
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"thickness": 0.15, "height": 2.5 } },
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{ "op": "create", "parentId": "level-1",
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"node": { "type": "wall", "start": [5.5, 4], "end": [10, 4],
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"thickness": 0.15, "height": 2.5 } },
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{ "op": "create", "parentId": "level-1",
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"node": { "type": "wall", "start": [5.5, 5.5], "end": [8, 5.5],
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"thickness": 0.15, "height": 2.5 } },
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{ "op": "create", "parentId": "level-1",
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"node": { "type": "wall", "start": [8, 4], "end": [8, 5.5],
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"thickness": 0.15, "height": 2.5 } }
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]
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}
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}
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```
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### 4. Define zones
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The agent declares the rooms so later queries and item placement can target
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them by name:
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```jsonc
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// tool: set_zone (called once per zone)
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{
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"name": "set_zone",
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"arguments": {
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"levelId": "level-1",
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"label": "Living / Kitchen",
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"polygon": [[0, 0], [5.5, 0], [5.5, 8], [0, 8]]
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}
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}
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// → { "zoneId": "zone-living" }
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{
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"name": "set_zone",
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"arguments": {
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"levelId": "level-1",
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"label": "Bedroom 1",
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"polygon": [[5.5, 0], [10, 0], [10, 4], [5.5, 4]]
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}
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}
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// → { "zoneId": "zone-bed1" }
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{
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"name": "set_zone",
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"arguments": {
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"levelId": "level-1",
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"label": "Bedroom 2",
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"polygon": [[5.5, 5.5], [10, 5.5], [10, 8], [5.5, 8]]
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}
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}
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// → { "zoneId": "zone-bed2" }
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{
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"name": "set_zone",
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"arguments": {
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"levelId": "level-1",
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"label": "Bathroom",
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"polygon": [[5.5, 4], [8, 4], [8, 5.5], [5.5, 5.5]]
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}
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}
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// → { "zoneId": "zone-bath" }
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```
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### 5. Cut doors and windows
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The agent uses `cut_opening` to add entry doors on each interior partition
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and windows on the south and east façades:
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```jsonc
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// tool: cut_opening (called once per opening)
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{
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"name": "cut_opening",
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"arguments": {
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"wallId": "wall-south", // perimeter wall [0,0] → [10,0]
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"type": "window",
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"position": 0.25, // 25% along centerline
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"width": 1.2,
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"height": 1.2
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}
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}
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// → { "openingId": "window-south-1" }
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```
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```jsonc
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{
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"name": "cut_opening",
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"arguments": {
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"wallId": "wall-bed1", // partition wall to Bedroom 1
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"type": "door",
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"position": 0.4,
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"width": 0.9,
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"height": 2.1
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}
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}
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// → { "openingId": "door-bed1" }
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```
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The agent repeats this for Bedroom 2's door, the bathroom door, and two
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more windows on the east façade.
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### 6. Validate and report
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```jsonc
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// tool: validate_scene
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{ "name": "validate_scene", "arguments": {} }
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```
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Response:
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```jsonc
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{ "valid": true, "errors": [] }
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```
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The agent then reads the scene summary for its response to the user:
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```jsonc
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// resource: pascal://scene/current/summary
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{ "uri": "pascal://scene/current/summary" }
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```
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The host displays the returned Markdown: 1 site, 1 building, 1 level, 8
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walls, 4 zones, 3 doors, 3 windows; usable area ~78 m²; perimeter ~36 m.
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### 7. Iterate
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The user follows up:
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> **User:** Swap the bathroom and bedroom 2 — I want the bathroom near the
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> entrance.
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The agent loads the `iterate_on_feedback` prompt and issues a single
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`apply_patch` that updates the polygon of `zone-bath` and `zone-bed2` and
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moves the corresponding partition walls. Because mutation goes through the
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Zustand store, the user can `undo` the change if they dislike it:
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```jsonc
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{ "name": "undo", "arguments": { "steps": 1 } }
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// → { "undone": 1 }
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```
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## Takeaways
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- Mutations batch inside a single `apply_patch` so that `undo` rolls back
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the whole logical change.
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- Zones are not walls — they're polygon annotations that make later queries
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(`find_nodes({ zoneId })`) and planning steps much easier for the agent.
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- The agent never needs to speak to `@pascal-app/viewer`: everything the
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host sees flows through tools + resources + prompts.
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@@ -0,0 +1,202 @@
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# Renovate an existing flat from photos
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This example shows how an agent can combine the `renovation_from_photos`
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prompt with the `analyze_floorplan_image` and `analyze_room_photo` vision
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tools to propose a renovation plan grounded in real photos.
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> **Note:** the vision tools use MCP sampling (`createMessage`), which
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> Claude Desktop supports today. Hosts without sampling support will get a
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> structured `sampling_unavailable` error; fall back to the text-only
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> `from_brief` prompt in that case.
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## The brief
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The user drops four photos into the chat:
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1. A floorplan PDF page (exported as PNG).
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2. A photo of the current living room.
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3. A photo of the current kitchen.
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4. An inspirational photo from a magazine — a minimal Scandinavian loft.
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And types:
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> **User:** Claude, help me plan a renovation. Here's the current plan and
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> two room photos. I want something like this Scandinavian reference —
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> open-plan, neutral tones, keep the footprint.
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## What the agent does
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The host loads the **`renovation_from_photos`** prompt:
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```text
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currentPhotos: ["data:image/png;base64,...", "data:image/jpeg;base64,..."]
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referencePhotos: ["data:image/jpeg;base64,..."]
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goals: "Open-plan living/kitchen, neutral tones, keep the footprint."
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```
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The prompt tells the agent to (1) analyze the floorplan, (2) analyze each
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room photo, (3) seed a scene from the floorplan, (4) compare against the
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reference, and (5) propose patches.
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### 1. Extract the floorplan
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```jsonc
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// tool: analyze_floorplan_image
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{
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"name": "analyze_floorplan_image",
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"arguments": {
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"image": "data:image/png;base64,iVBORw0KGgoAAAANS...",
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"scaleHint": "1 m grid, total footprint ~9.5 m × 7 m"
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}
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}
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```
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Under the hood, the tool issues an MCP sampling request to the host with
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the image and a structured prompt asking for walls, rooms, and
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approximate dimensions. The response is validated against the tool's
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output schema:
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```jsonc
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{
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"walls": [
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{ "start": [0, 0], "end": [9.5, 0], "thickness": 0.25 },
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{ "start": [9.5, 0], "end": [9.5, 7], "thickness": 0.25 },
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{ "start": [9.5, 7], "end": [0, 7], "thickness": 0.25 },
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{ "start": [0, 7], "end": [0, 0], "thickness": 0.25 },
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{ "start": [4.5, 0], "end": [4.5, 7], "thickness": 0.15 },
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{ "start": [4.5, 3.5], "end": [9.5, 3.5], "thickness": 0.15 }
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],
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"rooms": [
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{ "label": "Living", "polygon": [[0, 0], [4.5, 0], [4.5, 7], [0, 7]] },
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{ "label": "Kitchen", "polygon": [[4.5, 0], [9.5, 0], [9.5, 3.5], [4.5, 3.5]] },
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{ "label": "Bedroom", "polygon": [[4.5, 3.5], [9.5, 3.5], [9.5, 7], [4.5, 7]] }
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],
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"approximateDimensions": { "widthMeters": 9.5, "depthMeters": 7, "areaSqMeters": 66.5 },
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"confidence": 0.82
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}
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```
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### 2. Analyze the room photos
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```jsonc
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// tool: analyze_room_photo
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{
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"name": "analyze_room_photo",
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"arguments": { "image": "data:image/jpeg;base64,/9j/4AAQ..." }
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}
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```
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Response:
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```jsonc
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{
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"approximateDimensions": { "widthMeters": 4.4, "depthMeters": 5.8, "heightMeters": 2.5 },
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"identifiedFixtures": [
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{ "kind": "sofa", "approximatePosition": [2.2, 3.5] },
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{ "kind": "coffee-table", "approximatePosition": [2.2, 2.4] },
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{ "kind": "tv-unit", "approximatePosition": [0.3, 2.0] }
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],
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"identifiedWindows": [
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{ "wallHint": "south", "approximateWidth": 1.4, "approximateHeight": 1.5 }
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]
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}
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```
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The kitchen photo is analyzed the same way.
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### 3. Seed the scene
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The agent reads `get_scene`, confirms the default empty Site → Building →
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Level is present, and then batch-creates walls matching the floorplan:
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```jsonc
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// tool: apply_patch
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{
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"name": "apply_patch",
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"arguments": {
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"patches": [
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{ "op": "create", "parentId": "level-1",
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"node": { "type": "wall", "start": [0, 0], "end": [9.5, 0],
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"thickness": 0.25, "height": 2.5 } },
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/* ...remaining perimeter + partition walls from the vision result... */
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]
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}
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}
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```
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The agent then calls `set_zone` three times to seed the Living / Kitchen /
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Bedroom polygons from the floorplan rooms.
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### 4. Cut the identified openings
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For each window the vision tool reported, the agent calls `cut_opening`
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against the corresponding perimeter wall:
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|
||||
```jsonc
|
||||
{
|
||||
"name": "cut_opening",
|
||||
"arguments": {
|
||||
"wallId": "wall-south",
|
||||
"type": "window",
|
||||
"position": 0.5,
|
||||
"width": 1.4,
|
||||
"height": 1.5
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 5. Propose the renovation
|
||||
|
||||
Guided by the reference photo's analysis (bright neutrals, open plan,
|
||||
minimal furnishing), the agent proposes a single logical patch:
|
||||
|
||||
- Remove the partition wall between Living and Kitchen.
|
||||
- Relocate the kitchen island further west.
|
||||
- Delete the bulky TV unit item; leave the sofa and coffee table.
|
||||
- Re-label the merged zone `"Open-Plan Living / Kitchen"`.
|
||||
|
||||
All of that goes into one `apply_patch`:
|
||||
|
||||
```jsonc
|
||||
{
|
||||
"name": "apply_patch",
|
||||
"arguments": {
|
||||
"patches": [
|
||||
{ "op": "delete", "id": "wall-partition-living-kitchen", "cascade": false },
|
||||
{ "op": "update", "id": "zone-living", "data": { "label": "Open-Plan Living / Kitchen",
|
||||
"polygon": [[0, 0], [9.5, 0],
|
||||
[9.5, 3.5], [0, 3.5]] } },
|
||||
{ "op": "delete", "id": "zone-kitchen", "cascade": false }
|
||||
/* + item moves / deletes for the TV unit etc. */
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The user can walk back with `undo`; `redo` returns them to the proposal.
|
||||
|
||||
### 6. Sanity-check
|
||||
|
||||
```jsonc
|
||||
// tool: validate_scene
|
||||
{ "name": "validate_scene", "arguments": {} }
|
||||
// → { "valid": true, "errors": [] }
|
||||
|
||||
// tool: check_collisions
|
||||
{ "name": "check_collisions", "arguments": { "levelId": "level-1" } }
|
||||
// → { "collisions": [] }
|
||||
```
|
||||
|
||||
The agent reports a summary of the changes plus the approximate new
|
||||
usable area (from the summary resource), and the user opens the scene in
|
||||
`@pascal-app/viewer` to see the renovated 3D layout.
|
||||
|
||||
## Takeaways
|
||||
|
||||
- The vision tools only return **data**. They don't mutate the scene —
|
||||
the agent is explicit about every structural change via `apply_patch`.
|
||||
- Photos supply priors (approximate dimensions, fixture types) that a
|
||||
brief-only workflow can't. Combine them with `from_brief`-style
|
||||
prompts when the user has both a reference and concrete text goals.
|
||||
- All renovation steps are a single temporal step per patch, so the user
|
||||
can compare before/after with `undo` / `redo`.
|
||||
Reference in New Issue
Block a user