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

by io.rtkmotion
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get_spotter_cv

Geo-stripped commercial-truck image corpus for detection_or_ocr_model_training. 1877 de-identified crops, every one a verified truck (equipment=truck on all 1877). FMCSA-public identity labels (usdot + carrier) are resolved on 90 crops (4.8%) and null on the rest — treat them as a bonus, not the basis of the corpus. Returns labels (JSONL, one row per crop — no plate read, no geo, no timestamp) plus time-limited signed URLs to the de-identified truck crops (faces/persons + non-subject text + burned-in GPS blurred, and EVERY plate blurred including the subject truck's own; only painted carrier markings remain). Pay $50 USDC on Base (chain 8453, USDC 0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913) to RTK wallet 0x6C11F8a21f7ca922F483Ed21C3b6c2d9B305B10C; pass tx hash as payment_tx. Eth/Solana: /.well-known/x402. Images are paged — use offset to page through the corpus with the same payment_tx.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax image URLs to return per call (default 500).
offsetNoImage offset for paging through the corpus (default 0).
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 $50 USDC on Base to 0x6C11F8a21f7ca922F483Ed21C3b6c2d9B305B10C, then pass tx hash here.

Schema Changelog

Changes observed during successful MCP inspections.

  1. 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"
+}
  2. Added

TDQS

A4.3/5.0
Behavior5/5

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

With no annotations, the description carries the full disclosure burden and does so thoroughly. It reveals that only 4.8% of crops have resolved identity labels, that labels contain no plate/geo/timestamp, that images are de-identified with faces/plates/GPS blurred, that URLs are time-limited, and that payment is required.

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 each sentence carries real information: corpus contents, label coverage, privacy handling, return format, payment instructions, and paging. It is longer than average, but the complexity and absence of annotations justify the length.

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?

For a no-output-schema, no-annotation tool, this description covers what is returned, what is deliberately absent, how to pay, how to page, and the key data caveats. An agent has enough information to invoke the tool correctly and interpret the response shape.

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 coverage is 100%, so the baseline is 3; the description goes further by explaining how offset relates to paging through the corpus with the same payment_tx and reinforces the $50 USD C payment requirement. This adds useful operational meaning beyond the schema descriptions.

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 identifies the resource as a 1877-crop geo-stripped commercial-truck image corpus for detection/OCR training and explicitly states that it returns JSONL labels plus signed image URLs. It is clear and specific, though it does not explicitly distinguish itself from sibling tools like get_spotter_cv_sample or get_spotter_cv_prrofile.

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?

The description gives clear context: this is for building detection/OCR models from a verified truck-image corpus, and it explains the payment and paging flow needed to actually retrieve data. It does not provide explicit when-not-to-use guidance or name alternatives, but the intended use case is evident.

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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