Skip to main content
Glama

physpace_generate_equilibrium_scene

Generate multi-object 3D tabletop manipulation scenes with static gravity equilibrium and zero initial penetration for RL/VLA curriculum generation.

Instructions

Batch-synthesize a multi-object 3D tabletop manipulation workspace with guaranteed Frame-0 static gravity equilibrium (|Mg - F_IPC| < 1e-5 N) and zero initial penetration (d_0 > 0) for autonomous RL/VLA curriculum generation.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "physpace_generate_equilibrium_scene"
  }
}

Input Schema

NameRequiredDescriptionDefault
seedNoRandom seed for procedural tabletop layout.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It does add genuine value by disclosing the two success invariants (|Mg - F_IPC| < 1e-5 N and d_0 > 0), which are non-obvious behavioral guarantees. It omits what is actually produced (URDF/MJCF scene file, object list, dataset format), whether the process is deterministic given the seed, expected runtime/cost, and failure modes when a layout cannot satisfy the constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is a single dense sentence with no filler, and the core purpose is front-loaded before the qualifying constraints. It is jargon-heavy (Frame-0, F_IPC, d_0) but that vocabulary is appropriate for the physics/RL domain.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex generation tool with no annotations and no output schema, the description covers the input-side contract well but leaves the output side entirely implicit. An agent cannot tell what artifact is returned or what to do with it. The mention of "Batch" with no batch-size parameter is also an unaddressed ambiguity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There is a single parameter with 100% schema description coverage, so the schema already explains that seed controls procedural tabletop layout. The description adds nothing about seeding behavior (e.g., determinism, or the absence of a batch-count parameter despite the word "Batch-synthesize"). Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb+resource: "Batch-synthesize a multi-object 3D tabletop manipulation workspace," and qualifies it with two concrete physical guarantees (Frame-0 gravity equilibrium, zero initial penetration). This clearly distinguishes it from the sibling tools (inspect_object_safety_envelope, simulate_grasp_trajectory, compile_universal_asset, step_realtime_safety_shield). It stops short of naming an alternative explicitly, keeping it at 4.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The trailing clause "for autonomous RL/VLA curriculum generation" implies the intended use case, so an agent can infer this is a data-generation stage rather than a runtime/simulation tool. However, there is no explicit when-to-use vs when-not, no prerequisites, and no routing guidance relative to the siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.