mechanism_simulate_submit

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

With no annotations, the description carries the full burden and does well: it discloses the async submit-and-poll pattern ('Returns immediately', poll job_result(job_id)), the missing-dependency fallback shape ({ok:false, reason, install...}), and the install requirement. It omits failure/timeout modes and job lifecycle behavior, keeping it short of a 5.

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

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

Information-dense and front-loaded with the purpose and routing before the parameter grammar. The long param enumeration is justified by the 0% schema coverage, though the run-on formatting makes it less scannable than it could be.

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

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

With no output schema and no annotations, the description correctly documents return values (job_id, status, cache_hit, mobility_dof) and the polled job_result payload fields. This is strong coverage for a complex async tool, with the loop_closures param and job-failure handling being the only notable omissions.

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

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

Schema description coverage is 0% across 9 params, so the description must compensate and largely does, fully specifying the links tree fields, drivers formats for revolute/prismatic, obstacles, gears coupling formula, gravity default, and dt_s/duration_s. It leaves the loop_closures parameter entirely undocumented and 'base' only named without format, which are real gaps.

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

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

States a specific verb and resource ('Simulate a rigid-link mechanism's DYNAMICS with PyBullet, asynchronously') and immediately scopes it to the MBD family. It explicitly distinguishes itself from the sibling mechanism_kinematics by noting this is the dynamics path while kinematics provides closed-form gates.

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

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

Gives a clear directive: 'Use mechanism_kinematics first for the exact closed-form gates (DOF, Grashof, stroke)', naming the alternative tool and the condition that selects it. It does not spell out explicit when-not conditions or prerequisites beyond the extra-install requirement, so it falls just short of the top tier.

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