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

With no annotations provided, the description carries the full burden and excels: it discloses the paid nature ($0.03), x402 payment mechanism, caching strategy (7 days), the compute-first settle-after model with no charge for errors, and the behavior without an EVM_PRIVATE_KEY (returns 402 price terms). It also includes the return shape, which is critical given the absence of an output schema.

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 sentence serves a purpose: cost, caching, return format, payment mechanics, and key requirements. No fluff or repetition. It is structured with a clear flow from functionality to operational details, making it 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 complexity (paid API, caching, payment setup) and no output schema, the description compensates fully by enumerating the exact return fields, explaining the payment flow, and specifying the prerequisite (EVM_PRIVATE_KEY). It covers all critical aspects an agent would need to know to invoke and understand the result.

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. The description adds value by clarifying the query parameter: it's for physical-product queries and is normalized for caching. However, it does not add any information about the category parameter beyond what the schema already states, so the added value is moderate.

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 computes a 'demand verdict for any physical-product query,' specifying both the action (demand verdict) and the resource (physical-product query). It also distinguishes from the sibling gauge_demand_live by noting the 7-day cache, making the purpose and scope 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 provides clear context for when to use this tool (for demand verdicts on physical-product queries) and implicitly differentiates from gauge_demand_live by emphasizing caching and cost. However, it does not explicitly compare with other siblings or state when not to use it, so it lacks explicit exclusions or alternative recommendations.

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 so thoroughly: it discloses the $0.10 x402 cost, live Reddit search up to 50 posts, one LLM synthesis call, ~45s budget, timeout → no-charge 504, USDC-on-Base payment, compute-first/settle-after model, and EVM_PRIVATE_KEY requirement. This is exemplary operational 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?

Though dense, every sentence conveys a distinct operational fact—cost, live source, budget, parity with gauge_demand, timeout handling, payment settlement, and key requirement. No filler or redundancy; the structure is an efficient single paragraph.

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?

There is no output schema, but the phrase 'Same verdict shape as gauge_demand' anchors the return format to a sibling tool, which mitigates the gap. The description covers pricing, auth, timeout, and error behavior well, though it could be even more explicit about the actual verdict fields.

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 documents both parameters at 100% coverage, so the baseline is 3. The description adds meaningful context that the query drives a live Reddit search and corpus match, clarifying how the query is actually used beyond the schema text.

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 defines a demand-gauge tool backed by a LIVE Reddit search, and explicitly distinguishes it from the cached sibling gauge_demand via 'Same verdict shape as gauge_demand (cache always miss)'. The verb and resource are specific and the live-vs-cached contrast is immediately apparent.

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?

Names gauge_demand as the alternative and implies this tool is for live-demand situations by noting the cache always misses and that a live Reddit search is performed at request time. However, it stops short of an explicit 'use this when' statement or exclusion criteria.

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 behavior. It states the tool is 'Free' and describes the returned data fields, but it does not mention response structure, pagination, or any operational constraints. For a simple parameterless read-only tool, this is adequate but not rich.

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, with the core purpose front-loaded ('Category index with...'), followed by a separate 'Free.' sentence that adds cost context. Every word earns its place, making it efficiently 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 tool's simplicity (zero params, no annotations, no output schema), the description covers the essential return content and cost. It could be more explicit that it returns a list, but 'index' implies that. For a tool this simple, the description is sufficiently 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 tool takes zero parameters, so there are no parameter semantics to clarify. The baseline for 0 params is 4, and the description adds no conflicting information.

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 category index with specific fields (opportunityCount, topScore, lastUpdated), which distinguishes it from sibling tools that focus on opportunities or demand gauges. It is more specific than a mere restatement of the name, though it lacks explicit differentiation 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 implies this tool is for retrieving category-level aggregated data, but it provides no explicit when-to-use guidance or exclusions. Sibling tools are clearly different in focus, so the context implicitly suggests usage, but no direct alternatives are mentioned.

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 responsibility and succeeds. It discloses the paid nature ($0.02 via x402), the exact response behavior (up to 50 cards, each with teaser and summary), error behavior (no-charge 400 for invalid category), payment settlement details (USDC on Base, compute-first, settle-after), and the prerequisite EVM_PRIVATE_KEY. This is exemplary 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?

Every sentence adds essential information: pricing, return shape, required and optional parameters, error behavior, payment mechanics, and setup requirement. The description is front-loaded with the most critical fact (cost) and remains tightly structured with no filler, making it highly efficient for an AI agent 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?

There is no output schema, so the description compensates by explaining the return shape (up to 50 opportunity cards, each a teaser plus summary) and error responses. Combined with the parameter schema, payment details, and setup instructions, it gives a complete picture of expected behavior and prerequisites, leaving no major ambiguities.

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 already documents both parameters with 100% coverage, so the baseline is 3. The description adds value by explaining the consequence of an unknown/missing category (no-charge 400 listing valid categories) and reiterating the minScore 0-100 range, which provides operational context beyond the schema's static property definitions.

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 up to 50 opportunity cards per category, each with a teaser and summary. The plural 'opportunities' and category scope distinguish it from the sibling get_opportunity, and the specific verb 'get' plus resource 'opportunity cards in a category' 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 explicitly requires a category and mentions the optional minScore filter, giving clear context for when to use the tool. It does not explicitly name alternatives like get_categories or get_trending_opportunities, but the error handling hint ('unknown/missing category → 400 listing valid categories') implies how to recover from a missing category. This is clear context without explicit 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?

No annotations are provided, so the description carries the full burden. It discloses the payment fee, payment mechanism (USDC on Base), error handling (never charged for an error), and the key requirement, all of which are critical 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 dense but every sentence adds value: payment, output fields, key behavior, and error safety. It is well-structured with key info upfront.

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 no output schema, the description enumerates all returned fields (freqScore, intensityScore, wtpScore, etc.). It also covers cost, key requirement, and the no-charge-on-error behavior, making the tool fully understandable for a single-opportunity lookup.

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 for both id and slug, so the baseline is 3. The description repeats the 'exactly one' constraint but adds no new parameter syntax, format, or examples.

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 one FULL opportunity card by id or slug, listing specific output components. This distinguishes it from siblings like get_opportunities (plural) and get_trending_opportunities.

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 context: exactly one of id or slug must be supplied, and a live call requires EVM_PRIVATE_KEY; without it, the call returns 402 price terms. However, it does not explicitly compare to alternative tools or state 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?

The description discloses that the sample is 'fixed' (static, not changing) and 'free' (no cost), but it does not explicitly state whether the tool makes network requests, has side effects, or what the response contains. With no annotations, this is insufficient for a safe invocation.

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 short sentences with no redundancy. It front-loads the key information: the sample is real, fixed, and free.

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 zero-parameter tool, the description is minimal. It explains the sample is a card but doesn't specify the structure of the response or any usage context, which is a gap given there is no 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?

The input schema has no parameters, and schema coverage is 100%. The description adds no parameter semantics because none exist, but the baseline for zero-parameter tools is 4.

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 the tool as returning a 'real, fixed sample opportunity card' showing the 'full paid card shape,' which clarifies it's a demo/sample variant. The name also indicates 'get' and 'sample,' distinguishing it from real opportunity tools like get_opportunity.

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?

No guidance on when to use this tool vs alternatives. It does not mention that it's for testing, previewing, or as a fallback when real data is unavailable. The only hint is the word 'sample,' but it lacks explicit usage 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, the description carries full behavioral disclosure. It covers payment details (cost, currency, network), error handling ('never charged for an error'), prerequisites (EVM_PRIVATE_KEY), and response format (limited teaser fields). This is extensive and goes well beyond basic expectations.

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 and front-loaded with the most critical fact (paid $0.01). Every sentence adds essential information—what is returned, payment mechanics, error safety, and key requirement. No waste or 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?

There is no output schema, so the description explicitly lists the returned fields (id, slug, title, category, score, signalCount, lastSeen). It also covers payment, error handling, and prerequisites, making it fully self-contained for a tool of this 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?

The tool has zero parameters, so there are no input semantics to clarify. The description appropriately focuses on output and behavior, which is sufficient. Baseline 4 for zero-parameter tools is met.

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 the top 20 trending opportunity cards as teasers, with a specific list of fields. It distinguishes itself from sibling tools by explicitly noting 'no evidence; buy get_opportunity for the full card', making it unambiguous what this tool does versus 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?

It explicitly names get_opportunity as the alternative for full cards, guiding when to use this tool vs. that one. It also provides critical usage context: payment required via x402, need for EVM_PRIVATE_KEY, and behavior without a key (returns 402 price terms).

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