Spatial World Model (world-model-mcp)
SkillAI & modelsTeaches the agent to use the Spatial World Model MCP server to track entities, 3D/2D positions, spatial relationships, object permanence, and movement simulation.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Spatial World Model (world-model-mcp) skill
What this skill tells your AI
The instructions your AI receives, as published by putervision/world-model-mcp in .agents/skills/world-model-mcp/SKILL.md and read by ahel’s review.
This skill provides step-by-step guidance and operational patterns for interacting with @putervision/world-model-mcp.
1. Role in the PuterVision Triad
- Perception Layer (
vision-memory-mcp): Ingests images, detects bounding boxes, extracts OCR. - World Model Layer (
world-model-mcp): Maintains persistent 3D/2D coordinates, bounding volumes, topological relations, and object permanence. - Action/State Layer (
state-memory-mcp): Manages task execution DAGs, decisions, blockers, and milestones.
2. Core Operational Sequence
- Inspect World: Call
get_spatial_mapwithformat: 'summary'to check total entities and spatial bounding box. - Proximity Lookup: Call
query_entitieswithnear_positionandmax_distance, orentity_idfor direct lookup. - Ingest Perception: When new visual elements are detected, call
ingest_observation. - Collision Pre-Check: Call
simulate_movementbefore issuing action commands. - Update State: Call
record_outcomeafter actions complete.
3. Tool Reference (15 Tools)
| Tool Name | Key Inputs | Description |
|---|---|---|
update_entity | name, type, position, bounding_box, properties | Upsert entity into spatial memory |
query_entities | query, type, near_position, entity_id, include_history | Find entities by text search, proximity, or entity ID |
set_relation | source_id, relation, target_id, offset | Record spatial relationship (on, inside, near, etc.) |
get_spatial_map | region_id, format (json, gltf, obj, summary) | Export structured spatial layout or summary |
simulate_movement | entity_id, delta_position, mode, check_collisions | Predict movement path or compute navigation waypoints |
ingest_observation | observer_pose, detections, visual_state_id, reconcile | Ingest perception detections and reconcile frustum view |
get_expected_view | observer_position, observer_orientation, fov | Calculate visible entities from observer pose |
link_to_goal | task_id, entity_id, relationship, action | Associate entity with task or extract spatial slice |
record_outcome | action_name, success, resulting_position | Update world state after action completion |
manage_spatial_spec | action, name, bounds, constraints | Spatial SDD contract registration and verification |
create_evidence_pack | task_id, entity_ids, snapshot_ids | Immutable cryptographic evidence package |
use_spatial_blackboard | action, topic, sender, payload | Multi-agent coordination and mutex locks |
manage_snapshot | action, name, snapshot_a, snapshot_b, entity_id | Snapshots, diffs, undo mutations, and time-travel |
generate_game_inputs | entity_id, target_position, control_profile, action | Playwright game inputs and screen coordinate projection |
wait_for_spatial_state | entity_id, condition, threshold, timeout_ms | Polling and waiting for spatial state condition |
Signals
- GitHub stars
- 16
- Last commit
- Sep 2026
Advanced
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world-model-mcp- Source
- github.com/putervision/world-model-mcp