GripForge

Simulate and bake a flexible chain

gripforge_flexible_chain

Queue a persistent 240 Hz XPBD simulation using GripForge Scene Engine. Read flexible_chain_schema first. recipe version=1: preset whip|rope|tail, duration<=10s, fps=30|60, physics {length,segments,damping,bendCompliance,gravity,radius,floor,iterations}; choose motion [{time,position,direction,capsules}] OR source {asset:{assetId,revisionId},clip,mount_node,offset,axis,colliders}. Source assets are immutable and workspace-scoped. Returns a job ID; poll generation_read for a new draft skinned-chain GLB, sampled joints, recipe, physics metrics and Character Studio link. This is a neutral physics preview, not a Meshy weapon redesign or automatic rebind of the source weapon. No runtime world collision or seamless loop guarantee. Cancel/retry keeps completed steps. Does not replace or visually validate an existing asset. 0 credits; storage quota applies.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "recipe": "<recipe>"
    },
    "name": "gripforge_flexible_chain"
  }
}

Input Schema

Name	Required	Description
`recipe`	Yes
`workspace_id`	No	Workspace authorized by the current key. Returned scene links open this workspace.
`idempotency_key`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 20, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the annotations (which only hint at mutability), the description discloses asynchronous job semantics, polling via generation_read, immutability and workspace scoping of source assets, cancel/retry behavior, absence of runtime collision/seamless-loop guarantees, 0 credits, and storage quota implications. This is far more behavioral context than annotations provide.

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 but appropriately sized for an async tool with an open recipe object. The main action is front-loaded and most sentences carry unique operational information, though a few cautions overlap slightly in theme (e.g., not a weapon redesign vs. does not replace an existing asset).

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

Completeness5/5

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

Given the complexity, absent output schema, and sparse parameter schema, the description covers workflow prerequisites, exact recipe constraints, async job lifecycle, return value behavior, and cost/storage effects. An agent has enough context to call the tool correctly and know what to do with the result.

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

Parameters5/5

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

With schema coverage at only 33% and recipe being an open object with no internal schema, the description is the only source of meaning for the required parameter. It details recipe version, preset values, duration/fps limits, physics fields, and the motion-or-source choice, adding critical semantics that the schema does not provide.

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?

The description opens with a specific verb and resource: 'Queue a persistent 240 Hz XPBD simulation using GripForge Scene Engine.' It clearly defines the deliverable (a new draft skinned-chain GLB) and distances itself from sibling operations like Meshy weapon redesign or automatic rebind, making its purpose easy to distinguish.

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

Usage Guidelines4/5

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

It explicitly instructs to read flexible_chain_schema first and states exclusions: not a weapon redesign, not an automatic rebind, does not replace or visually validate an existing asset. This provides strong use-context, though it does not name a specific alternative sibling tool to use instead when those exclusions apply.

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

All GripForge tools Inspect

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