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

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 reveals the read-only nature implicitly via 'browse', discloses pricing expectation ('Free, no payment required'), and describes output behavior for each input case. It omits edge-case behavior (invalid path, pagination, error handling), but the core behavioral traits are transparent.

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, each delivering distinct value: what it browses, the path-hierarchy behavior, and the pricing guarantee. No filler or repetition of schema information; it is front-loaded and compact while packing meaningful detail.

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 a single optional parameter, no output schema, and no annotations, the description covers the essential scenarios (root, category, lesson) and even mentions pricing and MCP tool names, which is valuable contextual linkage to sibling tools. It could be more complete by addressing error cases or result formatting, but it adequately sets expectations for a browse operation.

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 adds meaning beyond the schema by explaining how different path formats (root, category, lesson) map to different result types, effectively providing semantics that the parameter description alone does not convey.

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 uses a specific verb ('browse') plus resource ('hierarchical catalog') and explicitly enumerates the dataset categories (motion-capture, threat-intel, rehab-biomechanics). It clearly distinguishes from sibling get_* tools by focusing on hierarchy traversal rather than file retrieval, and from search_catalog by presenting a path-based browse paradigm.

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 gives clear usage context by defining behavior for three path scenarios: no path (root categories), category path (children), and lesson path (file inventory, pricing, MCP tool names). It does not explicitly mention alternatives ('use instead of...'), but the path-dependent semantics imply when this tool is appropriate versus search or direct retrieval.

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 burden. It discloses performance specs (60 fps, 56-joint hierarchy, latency), payment requirements (USDC amount, chain, wallet, tx hash), and alternative payment methods (x402). It lacks error/failure behavior, but substantial behavioral context is present.

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 efficient, with each segment covering purpose/specs, payment, or trial alternative. It is front-loaded with the main purpose, though the payment details make it somewhat lengthy.

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 tool is complex with payment requirements and multiple parameters. The description thoroughly covers purpose, technical specifications, payment instructions, and alternative access. It doesn't describe the response format, but given the tool's nature, the BVH return is implied. Overall, robust for its 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 covers all parameters with descriptions, setting a baseline of 3. The description adds value by linking payment_tx to the $10 USDC payment, explaining x_payment via x402, and mentioning lesson_path defaults, going beyond schema details.

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 'BVH skeletal animation for robotic_grappling_training or imitation_learning', clearly identifying the tool's output and use cases. It also distinguishes itself by referencing the cheaper trial alternative, get_trial_bvh, differentiating from siblings.

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 names get_trial_bvh as a cheaper trial sample, providing a direct alternative and when-to-use signal. It doesn't compare with other siblings like get_mocap_sample, but the specified use case gives clear context for when this tool is appropriate.

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 return format (` .calib` file as text) and the 'FREE' aspect, which adds value. However, it does not mention potential error conditions or explicitly confirm a read-only operation, though the 'get' verb and output format imply this.

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, front-loaded with the key phrase 'FREE multi-camera calibration parameters', and every sentence contributes useful information without waste. It achieves a high signal-to-noise ratio.

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 getter with one optional parameter and no output schema, the description covers purpose, usage context, and return format. It could add a note about missing lesson behavior, but the overall completeness is sufficient for an agent to decide and 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?

The schema provides 100% coverage for the single optional parameter `lesson_path`, including an example and default behavior. The tool description adds no additional parameter meaning beyond the schema, so the baseline 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 identifies the tool as retrieving multi-camera calibration parameters (intrinsics and extrinsics) for the capture rig, using a specific verb+resource phrasing. It distinguishes from sibling tools by specifying the exact asset (calibration file) and context (reprojection, retriangulation, rendering).

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 the tool is 'required' for pipelines that reproject, retriangulate, or render against original capture geometry, providing clear when-to-use context. It does not name alternatives or exclusions, but the use case is unambiguous and distinct from sibling get_* tools.

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 the burden of behavioral disclosure. It reveals that the sample is free, limited to 5 seconds, and intended for validation. It also notes the paid full-file option. While it doesn't describe the exact return format or any potential side effects, it gives substantial context for a read-only 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?

Two concise sentences front-load the key facts (FREE, first 5 seconds) and pack in use cases and an alternative. Every word earns its place with no 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?

For a simple tool with two optional parameters and no output schema, the description fully covers what the tool does, its limitations, pricing, and use cases. It is complete enough for an agent to select and invoke it 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 coverage is 100% (both parameters have descriptions), so the baseline is 3. The description adds minimal extra meaning beyond the schema, though it does mention 'lesson fps', which subtly ties to lesson_path. No further parameter detail is needed given the self-descriptive 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's function: providing a free 5-second BVH motion capture sample. It includes specific details (frame count = 5 × lesson fps) and distinguishes it from the full-file alternative (get_bvh), 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 explicitly mentions use cases (validating format, joint hierarchy, data quality for sim_to_real_retargeting or action_recognition) and points to the alternative get_bvh for full files (20–120 seconds, $10 USDC). This provides clear guidance on when to use this tool versus the sibling tool.

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 transparency burden. It discloses that the tool is 'Free' and intended for pre-purchase evaluation, which adds some behavioral context. However, it does not mention response format, data completeness, or any operational limitations, leaving room for additional disclosure.

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, concise sentence that front-loads the primary content and use case. Every word adds value, with no redundancy or 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?

Given this is a simple get operation with one parameter and no output schema, the description sufficiently covers the tool's purpose, content, and usage context. It could mention the return format, but that is not critical for a free evaluation profile. The description is complete enough for an agent to select and invoke this tool appropriately.

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 description covers 100% of parameters, providing a clear example for case_path. The tool description adds general context about the content (demographics, injury history, medications) but does not elaborate on parameter-specific semantics beyond what the schema already offers, aligning with the 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 returns patient demographics, injury history, and medications for a rehab case. It differentiates from sibling tools by highlighting it is free and used for evaluating case relevance before purchasing reports, 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 explicitly instructs when to use this tool: 'evaluate case relevance before purchasing biomechanical reports' and specifies the use cases (insurance_claim_validation, PT_outcome_measurement). It does not directly name alternative sibling tools, but the context implies its role as a preliminary assessment, which is clear enough.

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 the burden of disclosing behaviors. It explicitly states the payment requirement ($0.25 USDC on Base, payment_tx), latency (<200ms), and output formats (JSON + TXT). This is significant behavioral context, though it does not mention error handling, rate limits, or authorization beyond the payment mechanism.

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 information-dense but well-structured: purpose, metrics, formats, latency, payment. It front-loads the core purpose and avoids filler. While slightly long, every sentence delivers necessary 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?

For a tool with no annotations and no output schema, the description covers the key aspects: what it does, what data it includes, output formats, latency, and payment. It could be more explicit about the exact return structure or provide a decision tree for siblings, but given the output format is only specified as JSON/TXT, it 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 coverage is 100% and each parameter has a descriptive example. The description adds extra semantic value by clarifying the payment flow (e.g., 'pass tx hash as payment_tx') and the x_payment alternative, which goes beyond the schema's static 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 produces a per-exercise biomechanical report for specific use cases (PT_outcome_measurement, medical_insurance_review), lists the key metrics, and distinguishes it from sibling tools that likely provide summaries or profiles. The verb 'get' with 'report' and the detailed content make 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 when to use it ('for PT_outcome_measurement or medical_insurance_review' and 'per-exercise') and provides context on what it returns. However, it does not explicitly name alternatives or say when not to use it, leaving some ambiguity relative to sibling tools like get_rehab_summary.

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 the full burden and does disclose that the sample lacks numeric values. However, it does not describe the actual response format, potential errors, or other behavioral aspects beyond the absence of numbers, leaving some ambiguity.

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 concise (four sentences) and front-loaded with the core purpose. Every sentence adds useful information: the free nature, the structural output, the upgrade path, and a concrete usage example. No redundant or wasted text.

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 sample tool with one optional parameter and no output schema, the description covers the essential aspects: what it returns (structure, categories, no numbers), the cost difference, and an example. It does not describe the exact response structure, but given the tool's simplicity and the pointer to the full version, it 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?

The schema already fully documents the single parameter with a description and default value. The description adds value by providing an example value, which helps the agent understand what a valid case_path looks like 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 shows the structure and metric categories of a clinical summary without numeric values, which is a specific verb+resource+scope. It distinguishes itself from get_rehab_summary by noting that the summary provides full numeric 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 recommends using get_rehab_summary for full numeric data and provides a concrete example case_path for trying the sample. It implies when not to use this tool (when numeric values are needed) but does not exhaustively cover all sibling tools.

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 the full disclosure burden and does an excellent job. It reveals the payment requirement ($0.50 USDC on Base with exact wallet address and tx hash parameter), latency (<300ms), output formats (JSON + TXT), and even alternative payment routes (Eth/Solana via .well-known/x402). No contradictions or hidden behaviors.

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 well-structured sentences lead with the core purpose, then content details, then operational constraints (formats, latency, payment). Every sentence earns its place, and the verbose payment details are necessary due to exact addresses and chain specifications.

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?

Despite lacking an output schema, the description adequately describes the return contents and formats. It omits potential error states and the x_payment alternative is tersely noted, but for a paid summary tool, the provided information is sufficient for correct 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?

The schema already provides 100% description coverage for all three parameters, including payment details for payment_tx. The description only repeats the payment_tx instruction without adding new semantic value beyond the schema, so the baseline of 3 applies.

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 a specific deliverable: 'Cross-exercise biomechanical summary' with explicit use cases ('insurance_claim_validation or rehab_progress_tracking'). It enumerates contained data (asymmetry indices, ROM, fatigue markers, clinical synthesis), which distinguishes it from sibling tools like get_rehab_profile or get_rehab_report.

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?

Clear context is provided by naming two target use cases, but it does not explicitly state when not to use the tool or name alternative tools for different scenarios. This is a minor gap versus the 5-level requirement for explicit when/when-not guidance.

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 thoroughly discloses payment requirements (amount, chain, token, wallet, transaction hash), compatibility (MoveIt/ROS, Isaac Sim), performance (60 fps, latency <500ms), and DOF counts per robot. This is exemplary transparency for a paid 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?

The description is dense yet well-structured, leading with purpose, then platforms, compatibility, performance, and payment details. Each sentence carries critical information without redundancy, making it 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?

Despite lacking an output schema, the description provides sufficient expectations: NPZ format, platform selection, performance metrics, and payment flow. It does not describe the exact delivery response (e.g., download link vs inline data), but the operational context is largely complete for an agent to decide whether and how to invoke the 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%, but the description adds meaning beyond the schema by explicitly instructing how to use the robot parameter ('specify via robot= param') and payment_tx parameter ('pass tx hash as payment_tx'). It also mentions future platforms in a file path, offering extra context not present in 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 opens with what the tool returns ('Multi-robot joint trajectories (NPZ)') and its intended use cases ('sim_to_real_retargeting or imitation_learning'). It distinguishes itself from siblings by listing supported platforms and pointing to the cheaper trial alternative, get_trial_robotarget.

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 clearly states the use cases and gives an explicit alternative for a cheaper trial: 'Cheaper $0.25 trial sample: call get_trial_robotarget.' It does not enumerate all sibling exclusions, but the context of when to use this tool vs the trial is clear.

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 carries the transparency burden and excels: discloses de-identification (faces/bystander plates blurred), conditional plate visibility rules, no plate interpretation distribution, commercial-only policy, and detailed payment requirements. Nothing contradicts annotations since none exist.

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?

Although longer than average, every sentence delivers essential information: use cases, output contents, de-identification policy, exclusions, payment details, and free alternative. No filler or redundant phrasing.

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, the description adequately explains what is returned (structured trail fields, signed URLs) and the conditions affecting plate visibility. Payment and access details are fully covered, making the tool contextually complete for a paid data service.

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% (all three params have detailed descriptions). The tool description adds little beyond schema—it mentions payment flows but largely restates what the schema already says, so baseline 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 'commercial-carrier sighting bundle' with structured trail and signed URLs, and lists specific use cases. It distinguishes from the sibling get_spotter_carrier_profile by noting it as a free evaluation 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?

Explicitly specifies use cases (carrier_identity_verification, fleet_movement_corroboration, cargo_fraud_investigation), excludes owner-operators, and points to get_spotter_carrier_profile for free evaluation, providing clear when-to-use and when-not-to-use guidance.

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 the transparency burden. It discloses that the tool is free, that it is a preview/assessment step before purchase, and enumerates the returned data categories. It does not explicitly state read-only behavior or potential error conditions, but the 'before purchasing' phrasing strongly implies a non-destructive, informational operation.

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 sentence that front-loads the core purpose and enumerates useful output fields and use cases. Every clause earns its place, with no redundant or filler content.

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 simple one-parameter input and no output schema, the description sufficiently describes both the input context and return contents (USDOT, FMCSA name, class, counts, geo coverage). It also explains the business purpose, making it complete for an agent to select and invoke the tool appropriately.

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?

There is one parameter, `usdot`, and the schema already provides a clear description and example ('USDOT number, e.g. 054283') with 100% coverage. The tool description mentions USDOT as part of the output but adds no additional semantic meaning to the parameter beyond what the schema already supplies.

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 returning a commercial-carrier sighting profile with specific data fields (USDOT, carrier name, class, counts, geo coverage). It distinguishes itself from sibling tools by noting it is a free 'before purchasing' evaluation, which separates it from likely full-data tools like get_spotter_carrier.

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 the tool: before purchasing a carrier's sighting bundle, and for carrier_identity_verification or cargo_fraud_investigation. It provides clear context but does not mention when not to use it or explicitly name alternative tools, so it stops short of a 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?

With no annotations provided, the description fully carries the transparency burden. It discloses de-identification specifics, label coverage limitations, absence of plate/geo/timestamp data, payment requirements with exact chain/contract/wallet, time-limited URLs, and paging with the same payment_tx. This goes well beyond a basic tool description and gives rich behavioral context.

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 and front-loaded with the core dataset purpose. Every sentence carries necessary information—corpus specifics, label caveats, return contents, payment details, and paging—without filler or repetition. The length is justified by the tool's payment and data-license complexity.

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 covers return format, payment, de-identification, and paging effectively. However, it does not specify the exact JSON object structure of the response or error/edge-case behavior (e.g., missing payment), which would make it fully complete for autonomous 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?

Input schema coverage is 100%, providing solid baseline parameter descriptions. The description adds operational meaning beyond the schema by explaining that offset pages through the corpus using the same payment_tx, and it clarifies the x402 payment alternative. This is modest but meaningful added value over 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 identifies the tool as returning a de-identified commercial-truck image corpus with JSONL labels and signed URLs for model training. It distinguishes itself from likely siblings like get_spotter_cv_sample by emphasizing the full 1877-crop corpus, though it does not explicitly name alternatives.

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 intended use case (detection_or_ocr_model_training) is stated, but there is no explicit when-to-use or when-not-to-use guidance, nor any mention of alternatives such as get_spotter_cv_sample or get_spotter_cv_profile. Usage context is implied rather than explicitly delineated.

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. The description discloses that the tool is 'Free' and specifies the type of information returned (image count, label coverage, de-identification posture). However, it does not explicitly state that it is a read-only operation or describe any limitations or side effects, leaving some ambiguity for an agent unfamiliar with the domain.

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 with no redundancy. It front-loads the core purpose ('commercial-truck detection corpus profile') and immediately provides key details (image count, label coverage, de-identification posture) and a usage context. Every word contributes.

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 simplicity (0 params, no output schema) and the sibling context, the description adequately covers the tool's purpose and content. It names the specific metrics returned and ties it to a practical scenario (evaluating before purchase). It could be more explicit about the response format, but that is not a major gap for a profile-type 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 0 parameters, so the baseline is 4. The schema fully covers this with an empty properties object, and the description adds value by explaining what the profile contains, which is more useful than any parameter details would be.

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 provides a 'commercial-truck detection corpus profile' with specific content: image count, label coverage, and de-identification posture. The use of 'profile' distinguishes it from raw data tools like get_spotter_cv, and the phrase 'before purchasing' ties it to an evaluative purpose.

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 gives a clear context: 'Free — evaluate the training corpus before purchasing for detection_or_ocr_model_training.' This tells the agent when to use it (when evaluating a corpus prior to purchase). It does not explicitly name alternatives or exclusions, but the context is sufficiently clear for a decision.

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 behavioral burden and does so exceptionally. It discloses the return payload (10 label rows + time-limited signed URLs), the de-identification applied (faces/persons + non-subject text + GPS blur), and the key caveat that identity fields will likely be null in this slice (4.8% match rate). Payment behavior and price are also fully stated.

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 dense paragraph with multiple clauses, but it is well-organized and front-loaded with the key 'TRIAL' identifier. The extra detail on corpus match percentages is somewhat verbose but serves to set expectations about null identity fields. Every major element (purpose, return format, payment, alternative) is present, though slightly more compact phrasing would earn a 5.

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 the description must explain return values, and it does: 'Returns 10 label rows + time-limited signed URLs to those 10 de-identified crops.' The tool's payment complexity and trial nature are fully covered, including the unlabeled-majority caveat and free-preview path. The context is complete for an agent to decide whether to invoke this tool 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 baseline is 3. The description adds meaningful context beyond the schema by naming the exact payment amount, chain, token address, and wallet, and by explaining that payment_tx is passed after sending USDC while x_payment is the preferred gasless option. This goes beyond the schema's terse field descriptions, though the schema already covers the basic parameters well.

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 opens with 'TRIAL' and clearly identifies the tool as a 10-crop sample of a geo-stripped truck detection corpus, explicitly distinguishing it from the full corpus (get_spotter_cv) and the free preview (get_spotter_cv_profile). The verb+resource+scope are specific and no ambiguity remains.

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 says to use this trial 'to evaluate crop + de-identification quality before the full corpus' and points to get_spotter_cv for the full corpus and get_spotter_cv_profile for a free preview. It also gives payment instructions and alternative x402 paths, making the when-to-use and alternatives clear.

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 the burden. It adds useful behavioral context: the tool is free and serves an evaluation purpose. However, it doesn't disclose read-only nature, output format, or potential limitations, leaving room for more 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 sentence that front-loads the tool's purpose and contents, followed by the free attribute and use case. Every word earns its place; 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?

The description covers what the profile contains, why to use it (evaluation before purchase), and that it's free. It doesn't detail return structure, but the listed components give a clear picture. No output schema exists, so the description sufficiently covers the tool's essentials.

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% — both 'region' and 'location' have descriptions with examples. The tool description adds no parameter-specific detail beyond what the schema already provides, so the baseline 3 applies.

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 monitored location profile with specific contents (assets, feed types, scan interval, term sets). This distinguishes it from sibling tools like get_threat_sample or get_threat_summary, which focus on different aspects.

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 phrase 'Free — evaluate coverage and data freshness before purchasing threat assessments' explains the intended use case and why an agent would select this tool. It doesn't explicitly name alternative tools, but the context makes the usage scenario clear.

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 the burden. It discloses that the tool is a free sample, returns live data, and omits flagged items/entities. It also mentions the data proves continuous updates. While it doesn't cover error handling or exact response format, the key behavioral traits and limitations are transparent.

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 plus an example, with every sentence earning its place. It is front-loaded with the core purpose and limitations, and the example usage is practical without extraneous details.

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 sample tool with one optional parameter and no output schema, the description fully covers what is returned, what is excluded, how to upgrade, and how to try it. It is complete for its intended purpose.

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 a clear description of the location parameter and default. The description adds value by offering a concrete example ('culpeper-town') and instructing how to discover other locations via browse_catalog, going beyond the schema's basic definition.

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 that this tool returns a free live threat assessment sample with specific data points (threat level, confidence score, event distribution, scan freshness). It distinguishes itself from get_threat_summary by explicitly noting the sample excludes flagged items or entities, and provides upgrade guidance.

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?

Explicit guidance is provided: use this for a free sample to verify data is live, and upgrade to get_threat_summary for full detail. It also gives example usage and directs to browse_catalog for all locations, making the intended context clear and distinguishing from 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 are provided, so the description carries the full burden. It discloses pricing per tier, latency (<200ms), payment requirements (USDC on Base with specific chain/wallet, or x402 on Eth/Solana), and the nature of the response (confidence-scored alerts, event distribution, enriched entities, etc.). This is substantial behavioral context beyond 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?

The description is front-loaded with purpose, then logically organizes pricing, latency, payment instructions, and discovery tip. It is somewhat dense but each sentence adds necessary operational detail.

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, the description provides high-level response components but doesn't detail the full response structure. It covers payment, tiers, latency, and use cases, and points to browse_catalog for further discovery, making it fairly complete for a payment-gated 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 the baseline is 3. The description adds payment flow context (wallet address, chain, tx hash as payment_tx) that supplements the schema, clarifying how to pay and at what price. It reinforces the tier enum values with costs.

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 'Location threat assessment' for specific use cases (risk_underwriting, facility_security_audit, insurance_risk_modeling) and differentiates lite vs full tiers. This is a specific verb+resource+scope, and while it doesn't compare to siblings directly, the purpose is unmistakable.

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 states when to use (for risk_underwriting, facility_security_audit, insurance_risk_modeling) and recommends browsing via browse_catalog first, implying a sequencing. It lacks explicit exclusions or direct alternative comparisons, but the context is clear.

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 burden. It thoroughly discloses payment requirements: amount ($0.25 USDC), chain (Base), wallet address, and the need to pass a transaction hash. It also mentions an alternative payment method (x402). Minor gaps exist around failure behavior or return format, but the core paid-access behavior is transparent.

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 efficient, front-loading the core purpose and then providing necessary payment details. It avoids fluff, though some payment information is duplicated in the schema. Overall, every sentence adds value.

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 paid sample tool with no output schema, the description is quite complete: it explains the sample content, duration, relationship to the full tool, and all payment instructions. The reference to 'Same format as full get_bvh' provides a pointer for return format. It could be more explicit about requiring at least one payment method, but the instructions are clear enough.

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%, so the baseline is 3. The description largely repeats payment details already present in the schema (e.g., payment_tx description includes sending USDC and passing the hash). It does not add new parameter semantics beyond what the schema provides.

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 a trial BVH sample ('5-second BVH skeletal sample') from a specific lesson, and explicitly differentiates it from the full version ('Same format as full $10 get_bvh'). This distinguishes it from sibling tools like get_bvh and get_mocap_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 states exactly when to use it: 'use to evaluate data quality before committing'. It also names the alternative (get_bvh) and indicates this is a cheaper trial, providing clear 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, 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.

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.

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.

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.

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.

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.

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

No annotations are provided, so the description carries the full disclosure burden. It comprehensively discloses the video specs (1080p, synced cameras), URL characteristics (15-min signed, Range support), and payment requirements (chain, token, wallet, tx hash, x402 alternative). This is strong behavioral transparency for a paid 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 dense but every element is functional. It is front-loaded with purpose, followed by specs and payment details. The payment address and chain information make it long, but not wasteful.

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 and no annotations, the description covers purpose, URL format, payment, and even Range support. It does not specify exact output formatting or error handling, but a knowledgeable agent can likely invoke the tool correctly. The return type (URL string vs object) is slightly ambiguous.

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%, giving baseline 3. The description adds meaningful context by explaining payment_tx and how it relates to the USDC payment, and mentions x_payment via x402. It also references lesson_path's default behavior, though it adds little beyond the schema for the view 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 identifies the tool as providing a multi-view grappling video as a 15-min signed streaming URL, with explicit use cases (pose_estimation_training or technique_analysis). This resource type and purpose distinguish it from sibling tools like get_bvh or get_calibration.

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 explicitly states intended use cases and detailed payment prerequisites, giving clear context on when to use the tool. However, it does not explicitly contrast with alternatives or state when not to use it, so it lacks 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 of behavioral disclosure. It reveals that the operation returns full lesson detail with pricing and is free, which is useful. However, it does not mention pagination, result limits, sorting, or any potential rate limits, leaving some uncertainty about the full behavior of a search across multiple datasets.

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 with the primary purpose front-loaded. The second sentence adds purpose-linked output and pricing information; 'Free' is a useful qualifier. No sentence is wasted, and the structure is easy to scan.

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?

Since there is no output schema, the description compensates by explicitly stating the return value is 'full lesson detail with pricing.' It omits any mention of result ordering or pagination, which could matter for a broad catalog search, but for a simple 3-parameter search tool with strong schema coverage, the description is largely 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%, establishing a baseline of 3. The description adds value by mentioning 'techniques' as a match surface beyond the schema's 'labels, path segments, and subject names,' and by tying verticals to dataset types. It enriches the query semantics without needing to restate every 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 opens with a specific verb and resource: 'Full-text search across motion-capture, threat-intel, and rehab-biomechanics datasets.' It also lists match criteria (labels, paths, subject names, techniques) and the return type (full lesson detail with pricing), clearly distinguishing it from sibling retrieval tools like browse_catalog or get_*.

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 usage: use this when you need to search across the catalog and get full lesson detail with pricing to proceed to purchase. It does not explicitly mention when not to use it or name alternatives like browse_catalog for browsing, but the context is clear enough for an agent to select it appropriately.

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