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Mechanism Simulate Submit

mechanism_simulate_submit

Simulate rigid-link mechanism dynamics with PyBullet asynchronously. Submit link tree and drivers, get job ID, then poll job_result for trajectories, torques, and collisions.

Instructions

Simulate a rigid-link mechanism's DYNAMICS with PyBullet, asynchronously (the MBD family; requires the mbd extra — pip install 'ankusdrive[mbd]'). Use mechanism_kinematics first for the exact closed-form gates (DOF, Grashof, stroke).

links is a tree: [{name, box_mm:[lx,ly,lz], mass_g, parent (link index, −1 = fixed base), joint_type ('revolute'|'prismatic'|'fixed'), joint_axis:[x,y,z], joint_at_mm:[x,y,z] (in the parent frame), com_mm:[x,y,z]}]. drivers: [{link, rate_dps}] (revolute) or [{link, rate_mm_s}] (prismatic). Optional obstacles ([{box_mm, at_mm}]) for through-motion contact, base, gravity (m/s², default [0,0,−9.81]), dt_s, duration_s. gears ([{link_a, link_b, ratio, axis?, max_force?}]) couples two revolute links by ω_b = −ω_a/ratio (ratio = Nb/Na for an Na/Nb external mesh) — the moving image of the gear-train ratio gate.

Returns immediately. If PyBullet is absent: {ok:false, reason, install, mobility_dof, n_links}. Otherwise {job_id, status, cache_hit, mobility_dof}; poll job_result(job_id) for {trajectories, orientations (per-link world quaternion, sampled with trajectories), max_torques, collisions_through_motion (with the sim time of each contact), reachable_envelope {bbox_mm}, mobility_dof}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
baseNo
dt_sNo
gearsNo
linksYes
driversNo
gravityNo
obstaclesNo
duration_sNo
loop_closuresNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations only convey non-read-only/non-destructive. The description adds the essential behavioral contract: the call returns immediately, the PyBullet-missing fallback shape, the job_id/status return, and the requirement to poll job_result. This is exactly the behavioral context an agent needs beyond the sparse annotations.

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?

The description is dense and front-loaded: purpose, prerequisite, parameter semantics, then output contract. Inline code formatting aids scannability. Some phrases such as 'the moving image of the gear-train ratio gate' add jargon without clear value, and the block is long, though justified by the tool's complexity.

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

Completeness4/5

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

Given no output schema and no parameter descriptions, the description supplies nearly everything: install prerequisite, workflow order, async/polling behavior, error fallback, and the full job_result payload. It would be fully complete if the `loop_closures` parameter were documented.

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

Parameters4/5

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

Schema description coverage is 0%, so the description does the heavy lifting. It gives units and structure for links, drivers, obstacles, base, gravity, dt_s, duration_s, and the gear formula. The only gap is `loop_closures`, which appears in the schema but is never explained in the description.

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

Purpose5/5

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

States a precise operation: simulating rigid-link mechanism DYNAMICS with PyBullet asynchronously. It distinguishes itself from the sibling mechanism_kinematics by explicitly directing users to run kinematics first for closed-form gates, so an agent can tell which tool to select.

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

Usage Guidelines5/5

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

Provides explicit workflow guidance: use mechanism_kinematics first for DOF/Grashof/stroke gates, then submit the dynamics simulation. Also states the required `mbd` extra installation and the async pattern of returning immediately and polling job_result.

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

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