ProductClank

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

Annotations indicate readOnlyHint=false and destructiveHint=false. The description adds transparency by detailing credit costs (e.g., 200 for replies, 300 for likes/repost), auto-detection of platform from URL, and that product_id tailors replies. It does not contradict annotations and provides behavioral context beyond what annotations give.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph that efficiently conveys the main purpose upfront then details parameters and usage. Every sentence adds value (costs, platforms, product_id guidance, confirmation requirement). It could be slightly more structured with bullet points but remains clear and concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 4 parameters (1 required) and no output schema, the description covers the purpose, all parameter semantics, credit costs, supported platforms, and a usage directive. It is complete enough for an agent to correctly invoke the tool without ambiguity.

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 meaning by explaining the effect of product_id (tailor replies with product name vs generic), clarifying action_type costs and platform restrictions (replies cost 200, likes/repost cost 300; repost only on Twitter/Farcaster). reply_guidelines is not elaborated but the description enriches understanding overall.

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: 'Rally the ProductClank community to engage with a specific social post' and 'Creates a boost campaign'. It specifies the verb (boost/engage), resource (social post), and distinguishes from sibling tools by detailing the boost mechanics and optional product linking, which is not covered by other tools like create_content_campaign.

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 guidance on when to use the tool (e.g., boosting a post) and includes a directive to confirm credit costs with the user. It also explains the optional product_id usage. However, it does not explicitly exclude alternatives or compare with sibling tools like create_content_campaign, missing some when-not-to-use 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?

Annotations already indicate readOnlyHint=true, so the description's additional context (returning balance and plan, cost examples) adds value without contradiction.

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, no wasted words, front-loaded with the main purpose. Excellent conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, but the description explains return values (balance, plan) and provides cost context, making it complete for a simple read 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?

No parameters exist, and schema coverage is 100%. The description rightly adds no parameter info; baseline 4 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 states a specific verb ('return') and resource ('credit balance and plan'), and distinguishes itself from sibling tools like 'create_content_campaign' by specifying when to use it.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use before launching a campaign' and provides cost examples, giving clear context. Does not state when not to use, but the guidance is sufficient.

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 key behavioral traits: it spends credits, AI auto-expands the brief and activates the campaign, and community submissions and winner selection happen externally. Annotations (readOnlyHint=false, destructiveHint=false) are consistent; no contradiction.

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, packing essential information into two sentences without any redundant or superfluous content. 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?

Given the tool's complexity (7 parameters, no output schema), the description covers all necessary context: purpose, prerequisites, cost, auto-activation, and external workflow. It is complete for safe and correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the description adds limited value for parameters. It mentions product_id as a prerequisite and campaign_message as the core brief, but does not elaborate beyond what the schema already provides. 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's purpose: to launch a content campaign rallying the community to create content for a product. It distinguishes itself from sibling tools like suggest_content_campaign (for preview) and search_products (a prerequisite).

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 when-to-use guidance, including prerequisites (product_id from search_products), cost (1000 credits), and a recommendation to preview with suggest_content_campaign and confirm cost with the user before calling.

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

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

Discloses auto-fill from URL, overriding behavior, reuse of existing listings, free cost, and no credits charged. Annotations already indicate non-readonly and non-destructive, and description adds 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?

Concise yet comprehensive paragraph front-loaded with purpose, then auto-fill, usage, reuse, cost, and user confirmation. 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?

Covers purpose, usage, parameters, return value (id), reuse logic, and cost. Despite no output schema, description fully prepares the agent. User confirmation advice completes the 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?

With 100% schema coverage, the description adds meaning by explaining minimum requirement (url), auto-fill and override behavior, and optional nature of socials. Enhances understanding beyond schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists a new product on ProductClank as a token-free listing, distinguishes from siblings like search_products, and explains reuse behavior. Specific verb and resource with unique context.

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 when to use: after search_products finds no match and user wants to run a boost/campaign. Also advises confirming details with user before calling.

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, so description doesn't need to restate. Description adds context about return format and the condition of content engine being on, which is valuable 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?

Two sentences, front-loaded with purpose and return format, then usage guidance. Every sentence earns its place with zero waste.

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, description fully specifies return fields and conditions. Complete for a list tool with simple output and clear 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?

No parameters exist, so schema coverage is 100%. The description adds no parameter info but provides return structure context. Baseline 4 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 it lists ProductClank content spaces with specific conditions (owned, delegated, managed, content engine on) and returns space_id and name. It is distinct from siblings like write_content_candidates.

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 this first to resolve space_id for write_content_candidates and confirm with the user, providing clear when-to-use and step-by-step 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?

Annotations provide readOnlyHint=true, so the description adds little extra behavioral context. The note about 'confirm the match with the user' is more a usage guideline than a behavioral trait. No new information beyond the schema and annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first states the core function, second provides usage context. Every word is purposeful, no redundancy, and the key information is 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?

The description covers the tool's purpose and return value (IDs), but it does not explicitly state that multiple results may be returned. Given the simplicity of the tool and the presence of annotations and schema, this is a minor omission. Otherwise 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?

Both parameters are fully described in the schema (100% coverage). The description mentions searching 'by name', which aligns with the 'query' parameter, but does not add new semantic detail 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?

Clearly states the verb 'search', the resource 'ProductClank products', and the action 'by name and return their IDs'. The second sentence explains the tool's role in campaign creation, distinguishing it from siblings like create_product or boost_post.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when to use: when a product_id is needed for creating or boosting a campaign. It advises 'use this to resolve it, then confirm the match with the user,' implying a workflow step. Does not explicitly list alternatives, but context makes it clear this is for searching existing products.

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 declare readOnlyHint=true, and the description reinforces that nothing is created and no credits are charged. It adds specifics about returns (AI-drafted campaign and affordability check), which annotations do not cover.

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-loaded with key purpose and usage, followed by essential context. Every sentence adds value 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?

Returns are described (campaign fields and affordability), which is adequate given no output schema. However, additional details about the exact return structure could enhance 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 covers all 7 parameters with descriptions (100% coverage). The description adds practical context like 'product_id from search_products' and clarifies campaign_message as the core brief, but doesn't significantly extend meaning beyond what the schema already 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 states 'Preview a content campaign for a product BEFORE launching it' and emphasizes it's free and nothing is created, distinguishing it from create_content_campaign. The verb 'preview' and resource 'content campaign' are specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly directs the agent to use this tool before launching, then call create_content_campaign. Also instructs to obtain product_id from search_products, providing clear when-to-use and 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?

Beyond annotations, description discloses: no credits charged, candidates are unreviewed drafts, nothing auto-published, up to 25 per call. No contradiction with annotations (readOnlyHint=false etc.).

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 unique value. Front-loaded with core action. No repetition or filler. Well-structured for quick scanning.

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 lack of output schema and just 2 required parameters, description covers lifecycle (unreviewed drafts, human review), capacity, and non-publishing behavior. Missing error handling details but acceptable.

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 parameters are already well-documented. Description adds minor context linking space_id to list_content_spaces but doesn't significantly extend meaning beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the tool drafts content candidates into a ProductClank space. Explicitly distinguishes from sibling create_content_campaign by specifying it's for the user's own pipeline, not community campaigns.

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 guidance: use list_content_spaces first to resolve space_id, notes this is for personal pipeline, and directs agent to create_content_campaign for community campaigns. Also states capacity limit of 25 candidates.

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