picoads

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

Annotations already establish readOnly/idempotent safety (readOnlyHint: true, destructiveHint: false). The description adds valuable behavioral context by specifying exactly what data fields are visible (inventory types, floor prices, audience details, publisher trust tiers), which helps the agent understand what information is exposed without contradicting the safety 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?

Three sentences efficiently structured: (1) high-level intent, (2) specific data returned, (3) usage guidance. No redundant words or tautologies. Every phrase earns its place by conveying unique information about the tool's function or output.

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 parameter structure (2 params, no nesting) and complete annotations, the description is appropriately complete. It compensates for the missing output schema by enumerating the specific data fields returned (inventory types, floor prices, etc.), giving the agent sufficient context to understand the tool's value.

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?

With 100% schema description coverage, the schema itself adequately documents both 'hub_id' and 'limit'. The description mentions 'in a hub' which loosely maps to the hub_id parameter, but adds no additional semantic details, format constraints, or validation rules beyond what the schema already provides. Baseline 3 is appropriate when schema coverage is complete.

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 'Browse[s] open asks' with specific details about what is retrieved (inventory types, floor prices, audience details, trust tiers). It explicitly mentions 'asks' and 'publishers', distinguishing it from sibling 'browse_bids' which would concern buyer bids rather than publisher offerings.

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 final sentence provides clear usage context: 'Use this to find the right publisher for your campaign and set a competitive bid price.' This establishes when to use the tool (campaign planning phase) and implies the workflow (browse first, then bid). However, it lacks explicit mention of alternative tools like 'post_bid' for the subsequent bidding action.

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?

While annotations declare readOnly/idempotent status, the description adds valuable behavioral context: it discloses the specific data fields returned ('unit prices, budgets, and targeting') and explains business logic implications for pricing strategy that annotations cannot 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?

Three well-structured sentences: value proposition, functional description, and usage guidance. Every sentence earns its place with zero redundancy. Front-loaded with user benefit ('what advertisers will pay you').

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 2-parameter read operation with full schema coverage and safety annotations, the description is complete. It compensates for missing output schema by documenting returned fields ('unit prices, budgets, targeting') and provides sufficient strategic context.

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?

With 100% schema description coverage, the schema fully documents both parameters. The description references 'hub' contextually, adding minimal semantic value beyond the schema. Baseline 3 is appropriate when structured documentation is complete.

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 specific verbs ('See', 'Browse') with clear resource ('open bids'), scope ('in a hub'), and value proposition ('what advertisers will pay you'). It effectively distinguishes from sibling 'post_bid' (creation vs. browsing) and 'browse_asks' (bids vs. asks).

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 explicit 'when to use' guidance for setting floor prices with strategic options ('maximize matches' vs. 'premium inventory'). Lacks only explicit 'when not to use' or named alternative tools (e.g., contrasting with 'browse_asks') to reach 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?

Beyond annotations (readOnly, idempotent), the description adds valuable behavioral context by enumerating specific match statuses included (pending deliveries, completed settlements, active disputes) and stating it returns 'all matches' when unfiltered.

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 efficient sentences with zero waste: first states action and resource, second clarifies scope and return content. Well front-loaded and appropriately sized.

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, description adequately explains return values by listing specific match categories covered. Suitable for a simple read operation with strong annotations.

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 has 100% description coverage with complete enum values listed; description provides business context for statuses but baseline 3 is appropriate when schema carries full documentation burden.

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 uses specific verb 'See' with resource 'matches' and clarifies 'your agent' scope, distinguishing it from sibling browse tools like browse_asks/bids which lack the ownership constraint.

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 by specifying 'your agent' and 'current matches', implicitly distinguishing from browsing public asks/bids, though it does not explicitly name sibling alternatives or when-not-to-use scenarios.

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?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description does not need to restate safety. It adds behavioral context by listing the specific outputs and suggesting a decision-making use case.

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 two sentences, front-loaded with the core function and outputs, then usage guidance. No unnecessary 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?

For a simple read-only check tool with one parameter and no output schema, the description is complete. It explains the purpose, what data is shown, and when to use 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 has 100% coverage for the single hub_id parameter, and the description does not add additional semantic detail beyond the schema's example.

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 uses specific verb 'Check' and resource 'earning potential in a hub', and lists specific outputs (supply/demand, pricing, revenue). It distinguishes from sibling tools like check_matches or check_publisher_stats by focusing on earning potential.

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 'Call this before deciding which hubs to serve recommendations from', providing clear context. While it doesn't list alternatives, the guidance is direct and actionable.

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?

Annotations already provide strong behavioral hints (readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: false). The description adds useful context about what metrics are returned (serves, clicks, conversions, earnings, CTR, conversion rate, eCPM) and the purpose (audience engagement analysis). However, it doesn't disclose rate limits, authentication needs beyond the required agent_id, or data freshness details. No contradiction with annotations exists.

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 with two sentences. The first sentence clearly states the purpose and return values. The second sentence provides usage guidance. There's no wasted text, and information is front-loaded. However, it could be slightly more structured by explicitly separating purpose from usage.

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 (analytics tool with 3 parameters), rich annotations (covering safety and idempotency), and no output schema, the description is moderately complete. It explains what metrics are returned and the tool's purpose, but doesn't detail output format, timezone handling, or how metrics are calculated. With annotations handling behavioral aspects, the description provides adequate but not comprehensive context.

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%, with all parameters well-documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions analytics generally but doesn't explain how parameters like 'days' or 'hub_id' affect the returned metrics. 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 states the tool's purpose: 'Check your publisher analytics' with specific metrics listed (serves, clicks, conversions, etc.). It distinguishes from siblings by focusing on publisher analytics rather than bids, matches, or recommendations. However, it doesn't explicitly differentiate from 'get_hub_stats' which might be a related analytics tool.

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 implied usage guidance: 'Use this to see how your audience engages with recommendations.' This suggests the tool is for analyzing audience engagement metrics. However, it doesn't explicitly state when to use this versus alternatives like 'get_hub_stats' or 'check_roas', nor does it mention any exclusions or 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?

The description aligns with annotations (read-only operation implied by 'Check' and 'Returns') and adds valuable behavioral context by listing the specific metrics returned (clicks, conversions, revenue, etc.). Since annotations already declare the operation is safe and non-destructive, the disclosure of return payload fields provides adequate additional 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 consists of three efficient sentences: purpose declaration, return value specification, and usage guidance. Every sentence provides distinct value without redundancy, presenting information in a logical order from what the tool does to when to use it.

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 compensates by enumerating the specific metrics returned (ROAS, CPA, CTR, etc.). Combined with complete parameter documentation (100% coverage) and clear safety annotations, the description provides sufficient context for correct invocation and result interpretation.

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?

With 100% schema description coverage, the structured schema already documents all four parameters (days, bid_id, hub_id, agent_id) including their purposes. The description does not add semantic meaning or formatting details beyond the schema, which is acceptable given the high schema coverage 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 uses specific verbs ('Check', 'Returns') and identifies the resource (ROAS/campaigns). It distinguishes from siblings like browse_bids or check_opportunity by focusing specifically on performance metrics (clicks, conversions, revenue, ROAS, CPA, CTR) rather than general browsing or matching.

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 the tool ('Use this to decide whether to increase or decrease your bids'), offering clear decision-making context for bid optimization. However, it lacks explicit exclusions or alternative tools for scenarios outside of bid adjustment decisions.

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?

Annotations indicate it's a write operation (readOnlyHint false) and non-destructive. Description confirms a state-changing registration but adds no further behavioral details (e.g., side effects, irreversibility). With annotations covering the core, the description adds minimal extra 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?

Two concise sentences, no fluff. First sentence states purpose, second provides prerequisite. Efficient and front-loaded.

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?

Describes the core flow but omits details about optional parameters (bootstrap_token, payment_signature) and return values. For a completion tool in a registration workflow, more guidance on optional fields and potential outcomes would improve 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?

Schema coverage is 100%, so each parameter already has a description. The tool description does not add any parameter-level clarification beyond what the schema provides, resulting in baseline score.

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 the tool completes agent registration using a signed wallet message. It distinguishes from register_agent by specifying the prerequisite. The action is specific and 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?

Explicitly instructs to call register_agent first, providing a clear prerequisite. Does not explore when not to use or alternative tools, but the given guidance is sufficient for proper usage.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, indicating a safe read operation. The description adds useful context about what is returned (activity stats, open bids, earning potential) and the intended decision-making purpose, which goes beyond the structured fields.

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 that front-load the primary action ('Browse the marketplace.') and immediately convey the tool's value. No unnecessary words or repetition.

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 what the tool returns (hubs with activity stats, open bids, earning potential) and its purpose. While it does not detail every return field, it is sufficient for a discovery tool with good annotations.

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 adds value by giving concrete examples of categories (travel, SaaS, finance, education) that illustrate the 'category' parameter, which enhances meaning beyond the schema 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 uses specific verbs ('Browse', 'See', 'Returns') and clearly identifies the resource ('marketplace hubs with activity stats'). It distinguishes from siblings like browse_asks and browse_bids by focusing on broad category discovery rather than individual transactions.

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 ('to decide which topics are worth recommending'), providing clear context. However, it does not explicitly mention when not to use it or list alternatives, though the contrast with sibling tools is implied.

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?

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds valuable context about what 'stats' specifically means in this domain (price distributions, fill rates, etc.) without contradicting safety annotations. Does not mention caching behavior or data freshness.

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: first establishes capability and return value, second establishes workflow timing. Information is front-loaded with the most important behavioral 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 single-parameter read operation, the description is complete: it compensates for the missing output schema by enumerating return fields (price distributions, fill rates, etc.) and differentiates from siblings. Could improve by mentioning response format or pagination.

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?

With 100% schema coverage (hub_id described with example 'defi-yield'), the schema carries the full burden. The description does not mention the parameter, but given the high schema coverage, this meets the baseline expectation without penalty.

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 specific verbs ('See exactly') and enumerates exact metrics returned (price distributions, fill rates, open demand, treasury balance), distinguishing this from sibling tools like browse_bids or check_opportunity by specifying it provides holistic hub economics rather than individual listings.

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 workflow context ('Use this before posting to find the best opportunity'), positioning it relative to post_bid. However, it lacks explicit negative guidance (when NOT to use) or mention of alternatives like check_opportunity for deeper analysis.

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?

Annotations indicate a safe, non-destructive, idempotent read operation. The description adds valuable behavioral context: earnings per recommendation, no registration required for calling, and registration for earnings tracking. This fully complements the 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?

Description is three sentences, effectively front-loading purpose and usage. The third sentence about registration could be integrated, but it does not detract significantly. Every sentence adds value, though slightly less structured than ideal.

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 explains return types (products, tools, services with live pricing) and labeling requirement. It covers earnings model but could benefit from more detail on output format or pagination. For a simple recommendation tool, it is fairly 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 covers 100% of parameters with descriptions. The description adds practical tips for 'topic' and 'context' parameters, improving usability beyond the schema. For 'max_results', no additional value is added, but overall the parameters are well-explained.

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 the tool returns sponsored recommendations relevant to user queries, including products, tools, services with live pricing. It distinguishes from siblings by its specific focus on sponsored content and earnings.

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?

Description explains when to use (present alongside organic answers, labeled 'Sponsored') and that it's free to call without registration. It does not explicitly discuss when not to use or contrast with siblings, but the context 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?

The description states that feedback is read and used to decide future inventory, providing behavioral context beyond the annotations which only indicate non-read-only and non-destructive. No contradiction with 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 four sentences, front-loaded with the purpose. It efficiently communicates the tool's intent and unique value, though it could be slightly more concise by combining sentences.

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 7 parameters and no output schema, the description provides sufficient context about anonymity, how feedback is used, and the types of questions asked. It covers the 'why' and 'what' adequately for a feedback 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 parameters are documented. The description adds some context by mapping the questions to specific parameters (e.g., 'did you find what you needed' maps to 'did_you_find_it'), but does not provide syntax or additional 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 is for providing feedback about picoads, using specific verbs like 'tell us what you think' and listing the questions it covers. It distinguishes from sibling tools which are about browsing, checking, and posting bids.

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 that feedback is anonymous and requires no registration, implying low barrier to use. It doesn't explicitly state when not to use or alternatives, but the tool is unique among siblings, so 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?

Annotations indicate readOnlyHint=false and openWorldHint=true. Description adds that the tool never changes the recommendation, shares commission, and requires registration to track earnings. This goes beyond annotations by explaining side effects and requirements.

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 five sentences long, front-loaded with the primary goal. Each sentence adds value (purpose, behavior, reassurance, registration info). Slightly longer than minimal but still 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?

For a tool with one parameter and no output schema, the description fully explains the behavior: monetization attempt fallback, no change to recommendation, and registration for earnings. No gaps identified.

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 single 'url' parameter is described in the schema with coverage 100%. The description adds context that this is the URL the agent chose and will be monetized if possible, which reinforces its purpose.

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 the tool earns commission on links already being recommended. It explicitly says it returns a monetized version if available, or the original URL otherwise. The purpose is distinct from sibling tools which focus on browsing, bidding, and registration.

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?

Description instructs to pass the outbound URL the agent chose and explains the conditional behavior. It does not explicitly state when not to use or list alternatives, but the context is clear enough 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?

Annotations provide minimal behavior info (readOnlyHint false, etc.). The description adds valuable behavioral context: 'You pay only the publisher's floor price (not your max), plus the hub fee. One bid can fill across multiple publishers.' This explains pricing and multi-fill behavior, which is beyond annotations. No contradiction with 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 extremely concise—four short sentences—with no wasted words. It is front-loaded with the core action 'Buy ad distribution in a hub.' and efficiently covers key 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?

Given the tool has 14 parameters, 6 required, no output schema, the description is fairly complete. It explains the pricing model and fill behavior. However, it does not describe what the response/return value contains (e.g., bid ID, status). Despite this, it provides enough for an agent to understand the core functionality.

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 baseline is 3. The description mentions 'budget, unit price, and targeting' but does not add meaning beyond the schema's existing property descriptions. It does not elaborate on any specific parameter 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 states the tool's purpose: 'Buy ad distribution in a hub. Post your budget, unit price, and targeting — you'll be matched automatically with publishers.' It uses a specific verb ('post') and resource ('ad distribution'), and distinguishes from sibling tools like browse_asks, browse_bids, and check_matches which are for viewing or checking rather than creating.

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 a clear context of when to use this tool (to buy ad distribution) and explains the pricing model, but it does not explicitly state when not to use it or mention alternative tools by name. However, the sibling tool names and implied functionality offer enough guidance for an agent.

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?

Annotations already provide idempotentHint=true and readOnlyHint=false. Description adds context that it's a free, one-time registration creating a permanent identity, but doesn't elaborate on idempotency or side effects beyond what annotations convey.

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, front-loaded with value proposition, no waste. Efficiently covers purpose, process, and next step.

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?

Missing output schema, but description implies return includes identity, API key, etc., and steps to complete registration. Adequate for a registration flow with sibling guidance.

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 baseline is 3. Description does not add additional meaning beyond what the schema provides for 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?

Description clearly states the tool registers an agent, providing identity, API key, earnings tracking, and USDC payouts. It distinguishes itself as step 1 of 2 from sibling complete_registration.

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 it's step 1 of 2 and directs to call complete_registration next. Mentions free registration and 30 seconds, giving clear context on when to use.

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