RTK Motion — Motion Capture, Biomechanics, and Threat Intelligence

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

No annotations provided, so description carries full burden. Discloses 'Free, no payment required' (cost transparency) and explains behavioral contract for different path inputs (what return structures to expect at root/category/lesson levels). Could mention read-only safety or auth requirements to reach 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?

Three sentences, zero waste. Front-loaded with purpose ('Browse hierarchical catalog...'), followed by path behavior specification, ending with cost constraint. Each sentence conveys distinct value (purpose, usage logic, cost).

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?

Given single parameter and no output schema, description compensates by detailing return values for different traversal levels (file inventory, pricing, MCP tool names). Covers domain context (motion-capture, threat-intel, rehab-biomechanics). Complete enough for selection and basic invocation.

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 coverage is 100%, but description adds significant semantic value beyond schema. Schema only states 'Catalog path to browse' with examples; description explains the functional mapping of different path patterns (no path→root, category→children, lesson→inventory/pricing/tools), clarifying the hierarchical navigation logic.

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?

Specific verb+resource: 'Browse hierarchical catalog of motion-capture, threat-intel, and rehab-biomechanics datasets.' Distinguishes from siblings by emphasizing navigation/discovery versus data retrieval (get_* siblings) or search (search_catalog). Domain specificity clarifies scope.

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?

Provides clear contextual guidance for the path parameter: 'No path → root categories. Category path → children. Lesson path → file inventory...' Implicitly differentiates from search_catalog by emphasizing hierarchical traversal, though explicit 'when to use search vs browse' statement would strengthen this to 5.

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

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

No annotations, so description carries full burden. It discloses data characteristics (60 fps, 56-joint hierarchy, latency <500ms) and payment requirement. However, it does not state whether the tool modifies state or return format, and error conditions (e.g., invalid payment) are not mentioned.

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?

The description is well-structured with purpose first, then details, then payment. Each sentence adds value, though the payment details are lengthy but necessary. No filler.

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?

Lacks output format specification (e.g., returns BVH file or raw data) and error handling. Does not explain default lesson_path behavior beyond schema. Provides good data context but misses return semantics.

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 coverage is 100%, so baseline is 3. Description adds no additional meaning to parameters beyond what the schema provides; it only repeats payment context. The subject and lesson_path parameters are not elaborated.

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?

The description states the tool provides 'BVH skeletal animation for robotic_grappling_training or imitation_learning' with specific details like 60 fps and joint hierarchy. It clearly indicates a paid animation data tool, distinguishing it from the trial sibling 'get_trial_bvh' mentioned in the description.

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?

Explicitly directs users to the cheaper trial version ('Cheaper $0.25 trial sample: call get_trial_bvh') as an alternative. Payment instructions are provided, implying this is for full access. No explicit when-not-to-use, but the sibling guidance is helpful.

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

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

With no annotations provided, the description bears full responsibility for behavioral clarity. It indicates a safe read operation (no destructive hints) and specifies the output format (text .calib file). It does not mention permissions or side effects, but for a retrieval tool, this is adequate.

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?

The description is two sentences long, with no wasted words. It front-loads the core purpose and provides necessary context in a compact form. Every sentence serves a clear function.

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?

For a simple retrieval tool with one optional parameter and no output schema, the description is sufficiently complete. It explains what the tool returns, when it is needed, and the output format. The term 'FREE' is slightly ambiguous but does not detract from completeness.

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?

The input schema has 100% description coverage for the single parameter 'lesson_path'. The tool description adds no additional semantic information about the parameter beyond what the schema already provides (default behavior and example). Baseline score of 3 is appropriate given high schema coverage.

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?

The description clearly identifies the tool as retrieving multi-camera calibration parameters (intrinsics and extrinsics) and specifies the output as a .calib file. It distinguishes itself from sibling tools like get_bvh or get_mocap_sample by focusing on calibration data needed for reprojection and rendering pipelines.

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?

The description explicitly states when the tool is required ('for any pipeline that reprojects, retriangulates, or renders against the original capture geometry'). While it does not mention when to avoid using it, the clear requirement context serves as good guidance. No alternative tools are mentioned, but the siblings are sufficiently different.

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

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

With no annotations provided, the description carries the full burden. It mentions 'FREE' and the frame count formula, but does not disclose authentication requirements, rate limits, or whether the call is side-effect-free. It adequately conveys the basic behavior but leaves gaps in transparency.

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?

The description is a single, compact paragraph that leads with the key value proposition ('FREE'), explains the sample size and formula, states its purpose, and contrasts with the paid alternative. Every sentence adds useful information without redundancy.

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?

The description covers the sample's nature (5 seconds, BVH), use cases, frame count derivation, and the existence of a full-file alternative. For a simple tool with no output schema and optional parameters, this is nearly complete. The only minor gap is not explicitly stating the output format (BVH) is a common skeletal animation format, but it's implied.

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?

Both parameters have schema descriptions (100% coverage), so the description adds little beyond schema. It implicitly ties subject and lesson_path to selecting the sample but doesn't elaborate on formats or constraints. Baseline 3 is appropriate as schema already documents the parameters sufficiently.

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?

The description clearly states the tool returns a free 5-second BVH sample, specifies the frame count formula, and distinguishes it from the paid full-file sibling tool get_bvh. It also mentions specific use cases (sim_to_real_retargeting, action_recognition), making the purpose unambiguous.

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?

The description implies use for validation/quality checks and contrasts with get_bvh for full downloads ('Full files are 20–120 seconds ($10 USDC via get_bvh)'), but does not explicitly state when not to use it or list alternative tools beyond get_bvh. It provides enough context for an agent to decide, but lacks exhaustive guidance on exclusions.

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

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

With no annotations provided, the description carries the full burden. It adds crucial cost context ('Free') and workflow sequencing (evaluate before purchasing), but lacks explicit safety confirmation (read-only status), error behavior, or return format details that would fully compensate for missing annotations.

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?

Two sentences with zero waste. Front-loaded with data contents, followed by cost model and usage guidelines. Every word earns its place.

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?

For a single-parameter lookup tool with no output schema, the description adequately covers what data is returned, the cost model, and the workflow relationship to other tools. It explains the return values (demographics, history, meds) despite lacking formal output schema.

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 100% (case_path is fully documented with example format), establishing baseline 3. The description adds no additional parameter context, but none is needed given the single, well-documented parameter.

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?

The description clearly states what data is returned (patient demographics, injury history, medications) and identifies the resource (rehab case). It distinguishes from sibling tools like get_rehab_report by positioning this as the free preliminary step versus purchased biomechanical reports.

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?

Excellent explicit guidance: 'evaluate case relevance before purchasing biomechanical reports' clearly states when to use this tool (before purchase) and implies the alternative (purchasing reports). It also specifies use cases: insurance_claim_validation or PT_outcome_measurement.

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

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

The description discloses latency (<200ms) and output formats (JSON+TXT), and notes payment is required. However, with no annotations, it fails to mention authentication, authorization, or side effects (likely read-only). The payment details are behavioral but incomplete.

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?

The description is front-loaded with purpose and then provides necessary details like metrics, latency, and payment. The payment section is verbose but critical for functionality. Overall efficient.

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?

Given no output schema, the description explains return formats and lists key metrics, which is sufficient for an AI to understand outputs. It covers latency and payment, but does not mention error conditions or failure scenarios.

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 coverage is 100%, so the baseline is 3. The description repeats payment info already in schema descriptions but does not add new meaning beyond what the schema provides for the parameters.

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?

The description clearly states the tool generates a per-exercise biomechanical report with specific metrics (CoM stability, rep detection, etc.) for PT or insurance review. However, it does not explicitly differentiate from sibling tools like get_rehab_summary or get_rehab_sample.

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?

The description mentions use cases (PT outcome measurement, medical insurance review), implying when to use it, but provides no guidance on when not to use it or alternatives. The payment instruction is specific but not about usage context.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: this is a free sample/demo tool that returns non-numeric structural data (not full numeric data), mentions data quality and coverage demonstration, and implies a commercial upgrade path. It doesn't cover rate limits or authentication needs, but provides substantial context for a sample tool.

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?

The description is extremely concise and front-loaded with the core purpose in the first phrase. Every sentence earns its place: the first establishes what the tool does, the second explains what it demonstrates, the third provides the upgrade alternative, and the fourth gives a usage example. Zero wasted words.

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?

Given this is a simple single-parameter tool with no output schema, the description provides excellent context about what the tool returns (structure and metric categories without numeric values), its demo/sample nature, and the upgrade alternative. The only minor gap is lack of explicit information about the return format or potential errors, but for a sample tool, this is reasonably complete.

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 100% for the single parameter, so the baseline would be 3. However, the description adds meaningful context beyond the schema by providing a specific example value ('Try case_path='PhysicalRehab/40-50/Male/LabrumTear'') and explaining this parameter defaults to a 'showcase case,' which helps users understand the parameter's purpose in practice.

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?

The description clearly states the tool's purpose: to retrieve a 'FREE rehab biomechanics sample' that 'shows the structure and metric categories of a clinical summary without numeric values.' It distinguishes from sibling tools by specifying this is a sample/demo version versus 'get_rehab_summary' for full data.

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?

The description explicitly states when to use this tool ('FREE rehab biomechanics sample — shows the structure and metric categories...') and when to use an alternative ('Upgrade to get_rehab_summary ($0.50) for full numeric data'). It also provides a specific example case path to try.

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

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

With no annotations, the description fully bears the burden of disclosing behavior. It details output formats (JSON + TXT), latency (<300ms), and a precise payment mechanism ($0.50 USDC on Base, with wallet and payment parameter). It does not mention side effects, but the read-only nature can be inferred.

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?

The description is front-loaded with the core purpose and includes necessary details without excessive wordiness. Each clause adds value (use cases, output content, formats, latency, payment). It could be slightly more concise, but it is well-structured.

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?

Given the lack of an output schema, the description provides a good overview of the return content (asymmetry indices, ROM, etc.) and covers key contextual details like formats, latency, and payment. It is sufficiently complete for an agent to understand what the tool does and how to invoke it.

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 coverage is 100%, so the input schema already documents all three parameters (case_path, x_payment, payment_tx) with descriptions. The tool description does not add new semantic information beyond what the schema provides, so the baseline score of 3 is appropriate.

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?

The description clearly states the tool returns a 'cross-exercise biomechanical summary' for specific use cases (insurance_claim_validation, rehab_progress_tracking) and lists concrete outputs (asymmetry indices, ROM, fatigue markers). This verb+resource combination differentiates it from sibling tools like get_rehab_profile or get_rehab_sample.

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?

The description explicitly ties the tool to two use cases—insurance claim validation and rehab progress tracking—giving clear context for when to use it. However, it does not explicitly contrast with siblings like get_rehab_report or get_threat_summary, leaving some implicit distinction.

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

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

No annotations provided, so description carries full burden. It discloses data format (NPZ), latency (<500ms), compatibility (MoveIt/ROS, Isaac Sim), and payment requirements (USDC on Base, wallet address). It does not explicitly state read-only nature or side effects, but covers key behavioral traits.

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?

The description is a single dense paragraph containing many details (purpose, platforms, payment, compatibility). While informative, it could be better structured with bullet points or shorter sentences for readability. Not overly long, but could be more concise.

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?

Given the tool's complexity (5 parameters, no output schema, no annotations), the description covers essential aspects: data type, latency, compatibility, payment process, and alternatives. It is sufficiently complete for an agent to invoke correctly.

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 100%, baseline 3. The description adds value beyond schema by elaborating on robot platforms (g1/h1 DOF details), payment methods (x_payment vs payment_tx), and lesson_path defaults. This extra context helps agents correctly populate parameters.

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?

The description clearly states the tool retrieves multi-robot joint trajectories for sim_to_real_retargeting or imitation_learning, specifying platforms (Unitree G1/H1) and parameters. It distinguishes from siblings like get_trial_robotarget by noting the cheaper trial alternative.

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?

The description explains when to use the tool (for retargeting/imitation learning, specific robots) and mentions the cheaper trial via get_trial_robotarget. It does not explicitly state when not to use, but provides sufficient context for an agent to decide.

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

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

Without annotations, the description fully discloses behavior: returns structured sighting trail and time-limited signed URLs, de-identifies images, requires payment of $5 USDC on Base chain with two payment options, and mentions disclosure policy exclusions. No contradictions.

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?

The description is a single paragraph that efficiently covers purpose, usage, return details, and payment. It is front-loaded with the key purpose, but could benefit from structured formatting. No superfluous information.

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?

Given no output schema, the description adequately explains the return value (structured sighting trail and signed URLs). It also covers payment flow and exclusions. Missing details on error handling or confirmation steps, but sufficient for selection and invocation.

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 coverage is 100%, yet the description adds significant value by explaining the usdot parameter with an example, detailing the two payment methods (x402 and legacy), and providing context for the payment_tx parameter. This goes beyond the schema.

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?

The description clearly states it's a sighting bundle for specific use cases like carrier_identity_verification, and distinguishes itself by specifying commercial carriers only and mentioning the free evaluation sibling. The verb 'get' and resource 'spotter_carrier' are well-defined.

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?

The description explicitly lists use cases (carrier_identity_verification, fleet_movement_corroboration, cargo_fraud_investigation), states exclusions (owner-operators), and provides alternative free evaluation via get_spotter_carrier_profile. It also gives payment instructions.

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

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

With no annotations provided, the description carries the full burden. It discloses the tool returns a profile with counts and geo coverage and states it's free. It does not mention authentication, errors, or rate limits, but for a simple read-only tool the coverage is adequate.

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?

The description is two sentences, no wasted words. Front-loaded with the tool's output and followed by usage context. Extremely concise and effective.

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?

Given the tool's simplicity (one required param, no output schema), the description covers the output elements and use cases adequately. It does not detail error conditions or format, but is sufficient for an agent to decide and invoke.

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?

The schema already fully describes the single parameter (USDOT number with example). The description adds context about what the output contains but does not enhance parameter semantics beyond the schema.

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?

The description clearly states the tool retrieves a commercial-carrier sighting profile with specific fields (USDOT, name, class, counts, geo coverage). It distinguishes from siblings like get_spotter_carrier and get_spotter_cv by emphasizing it's a profile with summary data.

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?

The description explicitly mentions use cases (evaluate sighting bundle before purchase, identity verification, fraud investigation) and notes it's free. However, it lacks explicit when-not-to-use guidance or alternatives among siblings.

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

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

No annotations provided, so description carries full burden. Discloses output format (JSONL, time-limited signed URLs), privacy handling (faces blurred, truck markings retained), payment process, paging via offset, and that images are de-identified. Thorough.

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?

Six sentences front-loaded with primary purpose, each sentence adds unique value (output format, privacy, payment, paging). No redundancy or fluff.

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?

No output schema, but description explains return format (labels JSONL, URLs time-limited signed) and covers all necessary behavioral aspects including payment and paging. Complete for a data retrieval tool.

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 coverage is 100%, baseline 3. Description adds context for payment_tx and x_payment (exact payment instructions, signed payload explanation) and explains limit/offset in context of paging, enhancing understanding beyond schema.

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?

The description clearly states the tool returns a geo-stripped truck-recognition CV/OCR training corpus with labels and de-identified image URLs, specific verb 'returns' and resource 'corpus' that distinguishes from siblings like get_spotter_cv_profile.

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?

Implied usage for accessing a dataset with payment and paging, but no explicit guidance on when to use vs siblings (e.g., get_spotter_cv_sample) or when not to use.

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

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

No annotations are provided, so the description carries full burden. It describes the output (image count, label schema, de-identification posture) and implies it is a read-only, free evaluation tool, but does not detail authentication requirements, rate limits, or other behavioral traits.

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?

The description is two concise sentences. The first sentence front-loads the core purpose and attributes, and the second provides context for usage. Every sentence earns its place.

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?

Given no output schema, the description sufficiently explains what the tool returns (image count, label schema, de-identification posture) and its purpose as a free evaluation tool. The context is complete for a simple, parameterless tool.

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?

The tool has no parameters, so schema coverage is 100%. The description does not need to add parameter information. It adds value by explaining the output, which is appropriate given no parameters.

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?

The description clearly states it provides a profile of a truck-recognition CV/OCR corpus including image count, label schema, and de-identification posture. It distinguishes itself from sibling tools by explicitly stating it is a free evaluation tool for the corpus before purchasing.

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?

The description indicates the tool is meant for evaluating the training corpus before purchasing for model training, giving clear context for when to use it. However, it does not explicitly name alternatives or when not to use it.

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

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

Discloses trial nature, returns 10 rows with signed de-identified URLs, payment details for USDC on Base, and mentions time-limited URLs. No annotations, so description carries burden; covers key behaviors but misses edge cases like payment failure.

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?

Description is comprehensive but concise given the complexity; packs many details into 4 sentences without redundancy. Could be slightly more structured.

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?

Covers trial purpose, output description, payment instructions, and de-identification details. No output schema, but return format is clearly described. Completes the context for this payment-gated trial tool.

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 coverage is 100% with descriptions. Description adds context beyond schema by detailing payment methods, wallet address, chain, and fallback, though schema already covers basics.

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?

Clearly states it's a trial slice of a truck-recognition CV/OCR corpus for evaluation before purchasing the full corpus. Distinguishes from siblings like get_spotter_cv and get_spotter_cv_profile.

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?

Explicitly tells when to use (evaluate label and de-identification quality) and names alternatives (get_spotter_cv for full corpus, get_spotter_cv_profile for free preview).

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

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

With no annotations provided, the description carries full disclosure burden. It successfully conveys cost ('Free') and evaluative purpose ('evaluate coverage and data freshness'), but omits explicit read-only confirmation, auth requirements, or rate limits that would fully establish safety profile.

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?

Two dense sentences with zero waste. First sentence uses em-dash to front-load returned data components; second sentence delivers business logic and purchasing context. Every clause earns its place.

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?

Lacks output schema, but description compensates by enumerating profile components (assets, feeds, intervals, terms) and business model context. Deducted one point for not explicitly confirming non-destructive read behavior given 'threat' domain sensitivity.

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 has 100% description coverage (region and location both documented), establishing baseline 3. Description adds implicit context that 'location' refers to a monitored site via 'Monitored location profile,' but does not elaborate parameter formats beyond schema.

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?

The description identifies specific resources retrieved (assets, feed types, scan interval, term sets) and the target (monitored location profile), functioning as a concrete noun phrase. However, it could more explicitly distinguish from sibling 'get_threat_summary' by clarifying this returns raw configuration/data versus aggregated analysis.

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?

Explicitly states this is a free evaluation tool to use 'before purchasing threat assessments,' directly naming downstream workflows (risk_underwriting, security_operations). Provides clear temporal/business logic for when to invoke versus paid alternatives.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: 'FREE live threat assessment sample' indicates no cost and real-time data, 'Proves data is live and continuously updated' clarifies freshness, and 'No flagged items or entities' sets expectations on output limitations. However, it lacks details on rate limits, authentication needs, or error handling.

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?

The description is appropriately sized and front-loaded, with every sentence earning its place. It starts with the core functionality, adds behavioral context, provides usage guidelines, and ends with practical examples—all without redundancy. The structure is efficient and easy to parse.

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?

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is mostly complete. It covers purpose, usage, behavioral traits, and parameter context. However, it lacks explicit information on output format or error cases, which could be helpful for an agent, though not strictly required given the simplicity.

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?

The input schema has 100% description coverage, so the baseline is 3. The description adds value by explaining the parameter's purpose beyond the schema: it provides an example ('culpeper-town'), mentions it's a 'location slug,' and notes it 'Defaults to showcase location,' which clarifies usage context not in the schema. This elevates the score above baseline.

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?

The description clearly states the tool's purpose with specific verbs ('get threat assessment sample') and resources ('current threat level, confidence score, event distribution, and scan freshness for a monitored location'). It distinguishes from sibling tools by explicitly mentioning 'No flagged items or entities (upgrade to get_threat_summary for full detail)' and referencing browse_catalog for location discovery.

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?

The description provides explicit guidance on when to use this tool versus alternatives: 'No flagged items or entities (upgrade to get_threat_summary for full detail)' tells users when to choose the sibling tool. It also suggests usage context with 'Try location='culpeper-town' or browse_catalog path='ThreatIntel' for all locations,' offering practical examples and prerequisites.

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

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

No annotations provided, so description carries full burden. It discloses latency (<200ms), payment details (USDC on Base, wallet, two payment methods), and output content per tier. Lacks info on failure modes or idempotency.

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?

Description is dense but well-structured: purpose, pricing, outputs, then payment. Each sentence adds value; could be slightly trimmed but efficient overall.

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?

No output schema, but description details output contents per tier. Covers prerequisites (browse_catalog), payment process, and use cases. Missing error handling or data freshness, but substantial for the complexity.

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 coverage is 100%, but description adds significant value: explains tier outputs, payment workflow for payment_tx, and hints at region dependency. All parameters are meaningfully elaborated beyond schema descriptions.

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?

Description clearly states specific verb+resource: 'Location threat assessment' and lists targeted use cases (risk_underwriting, facility_security_audit, insurance_risk_modeling), distinguishing it from sibling tools like get_threat_profile.

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?

Explicitly advises 'Browse ThreatIntel via browse_catalog first', providing context for when to use this tool. Also differentiates lite vs full tiers, but does not explicitly state when not to use it or alternatives.

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

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

No annotations exist, so description carries full burden. It discloses the trial nature, exact cost ($0.25 USDC), target wallet and chain, and payment methods. Lacks details on what happens on failure or idempotency, but sufficient for a trial data retrieval tool.

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?

Front-loaded with key info (TRIAL, sample size). While lengthy due to payment details, every sentence adds value. Could be more concise by separating payment into a structured field, but acceptable.

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?

No output schema, but description specifies return format (300 frames @ 60fps), ties to full get_bvh for consistency, and covers all prerequisites (payment, subject options). Complete for a trial tool.

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 coverage is 100%, so baseline 3. Description adds context: default subject value, explanation of x_payment as a signed authorization, and explicit instruction for payment_tx. This goes beyond bare schema descriptions.

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?

The description clearly states it returns a '5-second BVH skeletal sample (300 frames @ 60fps)' from a specific lesson, and contrasts with sibling 'get_bvh' as a trial version. The verb 'get' and resource 'trial_bvh' match the tool name, and it distinguishes from the full version by calling it a 'sample'.

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?

Explicitly says 'use to evaluate data quality before committing' and notes 'Same format as full $10 get_bvh', telling the agent when to use this trial vs the full tool. Provides clear payment instructions and alternatives (x402 vs payment_tx).

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

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

No annotations are provided, so the description carries full burden. It discloses payment requirements ($0.25 USDC), wallets, robots supported, and format. However, it does not mention error handling or what occurs if payment fails.

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?

The description is dense but well-structured: starts with purpose, then format, price, and payment details. Every sentence adds value, though slightly long. Could be more scannable with bullet points.

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?

No output schema exists, so description should explain return values. It mentions format (NPZ, frames, fps) but not structure of the data. Also lacks details on error states or behavior for invalid payment. Adequate for a trial tool but gaps remain.

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 coverage is 100%, but description adds significant value: specifies defaults for robot and subject, explains payment_tx must be for exact amount to specific wallet, and describes x_payment as alternative. This goes beyond bare schema.

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?

The description clearly states it's a 5-second robot-trajectory sample (300 frames @ 60fps NPZ) from a specific lesson, same format as the full $15 get_robotarget, explicitly distinguishing it as a trial for evaluating retargeting quality.

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?

It explains the tool is for evaluating retargeting quality before committing, contrasts with the full get_robotarget by price and sample length, and describes payment methods. It lacks explicit when-not-to-use, but context implies it's a test alternative.

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

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

With no annotations, the description carries full burden. It discloses key behaviors: multi-view synced cameras, 1080p, 15-min signed URL with Range support, and payment requirements ($5 USDC on Base, two methods). This provides substantial context beyond basic functionality.

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?

The description is front-loaded with purpose, then technical specs, then payment details. Every sentence is necessary given the payment complexity, though it could be slightly more concise by merging payment explanations.

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?

Given the complexity (payment integration, multiple parameters) and lack of output schema, the description adequately covers usage, payment, and default behavior. However, it doesn't specify the exact response format (e.g., URL string) or error handling for invalid payment.

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 coverage is 100%, but description adds value: explains preferred payment method (x_payment as gasless), details the EIP-3009 approach, and clarifies lesson_path defaults. This enriches understanding beyond schema descriptions alone.

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?

The description clearly states the tool retrieves a multi-view grappling video for 'pose_estimation_training or technique_analysis,' specifying 1080p synced cameras and 15-min signed streaming URL. This distinguishes it from siblings like get_bvh (3D motion data) and get_mocap_sample (mocap data).

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?

Usage context is implied through the purpose (e.g., for training or analysis), but no explicit when-to-use or when-not-to-use guidance is given compared to other sibling tools. The payment instructions are clear constraints, but alternatives are not discussed.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns pricing information and is free, but lacks specifics on rate limits, return format, or whether the search is read-only. However, the description adds valuable context beyond what annotations would provide.

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?

The description consists of two concise sentences, each adding distinct value: scope of search and key return detail (pricing). No fluff and front-loaded with the purpose.

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?

Given 3 parameters and no output schema, the description is fairly complete: it explains what is searched, what is matched, and what is returned (full detail + pricing). Missing details like pagination or result limits, but overall adequate for agent invocation.

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 coverage is 100%, so the schema already documents all three parameters. The description adds marginal value by stating it searches across specific datasets and matches labels/paths/subjects/techniques, but this is largely redundant with the schema's description of the query parameter.

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?

The description clearly states the verb ('search') and the resource ('catalog' across three specific domains). It distinguishes from siblings like 'browse_catalog' by emphasizing full-text matching and return of lesson details with pricing.

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?

The description implies when to use this tool: when you need to search and then directly call purchase tools. It provides clear context but does not explicitly state when not to use it or list alternatives among the many sibling tools.

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