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Glama

RTK Motion — Motion Capture, Biomechanics, Threat & Fleet Intelligence

by io.rtkmotion

get_trial_robotarget

TRIAL — 5-second robot-trajectory sample (300 frames @ 60fps NPZ) from the Kimura side-control trial lesson. Same format as full $15 get_robotarget — use to evaluate retargeting quality before committing. Platforms: Unitree G1 (43 DOF: 29 body + 14 hand), Unitree H1 (19 DOF), Apptronik Apollo (32 DOF). Pay $0.25 USDC on Base (chain 8453, USDC 0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913) to RTK wallet 0x6C11F8a21f7ca922F483Ed21C3b6c2d9B305B10C; pass tx hash as payment_tx. Eth/Solana: /.well-known/x402.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
robotNoTarget robot: 'g1' (default, Unitree, 43 DOF = 29 body + 14 hand), 'h1' (Unitree, 19 DOF), or 'apollo' (Apptronik, 32 DOF)
subjectNoSubject name: 'subject_1' (default) or 'subject_2'
x_paymentNoPreferred (x402 exact / gasless): base64 X-PAYMENT payload — a signed EIP-3009 transferWithAuthorization for this tool's USDC price to our wallet. Redeemable only by its signer; no gas needed.
payment_txNoUSDC transaction hash. Send $0.25 USDC on Base to 0x6C11F8a21f7ca922F483Ed21C3b6c2d9B305B10C, then pass tx hash here.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / robot / description
      Previous value: -"Target robot: 'g1' (default, Unitree, 24 DOF) or 'h1' (Unitree, 19 DOF)"New value: +"Target robot: 'g1' (default, Unitree, 43 DOF = 29 body + 14 hand), 'h1' (Unitree, 19 DOF), or 'apollo' (Apptronik, 32 DOF)"
  2. Changed1 schema field changed
    • addedInput schema / properties / x_payment
      Added value: +{
+  "description": "Preferred (x402 exact / gasless): base64 X-PAYMENT payload — a signed EIP-3009 transferWithAuthorization for this tool's USDC price to our wallet. Redeemable only by its signer; no gas needed.",
+  "type": "string"
+}
  3. Added

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full transparency burden. It discloses sample length, frame rate, NPZ format, supported robots with DOF counts, required USDC payment amount, chain, wallet address, and the need to pass payment_tx. It does not describe the NPZ return structure or possible failure modes, but the format reference to get_robotarget partially mitigates that gap.

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

Conciseness5/5

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

The description is a single dense paragraph that fronts the purpose with 'TRIAL —' and then packs platform and payment details without fluff. Every clause adds operationally important context for correctly invoking this paid trial tool.

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?

For a paid tool with no output schema and no annotations, the description provides an unusually complete picture: purpose, format, platform options, exact payment steps, and a pointer to the full tool for format expectations. It omits the internal NPZ structure and error behavior, but these are not critical for a trial evaluation use case.

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?

Schema coverage is 100% and each parameter already has a detailed description, so the baseline is 3. The description adds payment orchestration details (USDC amount, chain, wallet, passing tx hash, x402 alternative) that supplement the payment-related parameters beyond what the schema states, but it does not significantly enrich robot or subject semantics.

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 clearly identifies the tool as a TRIAL 5-second robot-trajectory sample (300 frames @ 60fps NPZ) from a specific lesson, and distinguishes it from the full get_robotarget by naming the full $15 version and the intent to evaluate before committing. This gives a specific verb, resource, and scope, and differentiates from sibling tools.

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?

The description explicitly states when to use it: 'use to evaluate retargeting quality before committing,' and contrasts it with the full-priced sibling get_robotarget. It also enumerates supported platforms and detailed payment prerequisites, giving clear context for invocation.

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

All RTK Motion — Motion Capture, Biomechanics, Threat & Fleet Intelligence toolsInspect
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TDQS

A4.1/5.0
Disambiguation4/5

Most tools are clearly separated by domain and tier (profile/sample/full), but get_mocap_sample and get_trial_bvh both return 5-second BVH samples and could be misselected. The rehab sample/profile tools are also close in evaluation purpose, though descriptions do help distinguish them.

Naming Consistency5/5

The data-access tools consistently follow get_<domain>_<variant>, and the only navigation tools are browse_catalog and search_catalog, which also use a predictable verb_noun pattern. There is no mixing of naming styles or inconsistent verb forms.

Tool Count3/5

With 21 tools, the set is borderline heavy for a data marketplace, especially since many tools are trial/sample/profile variants of the same paid products. The broad multi-domain scope justifies some size, but several variants could be consolidated without much loss of clarity.

Completeness4/5

The tools cover catalog browsing/searching, free profiles and samples, and paid full products across motion capture, rehab biomechanics, and threat/fleet intelligence. Minor gaps exist, such as no video trial and no generic fetch-any-catalog-item tool, but agents can work around these with the existing sample tools.

Resources