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 that the tool returns a landing_id and editor_url, which is a useful outcome, but it does not mention any other behavioral traits like authentication requirements, side effects, or whether the operation is reversible.

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, front-loaded with the action and resource, with no wasted words.

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

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

The description covers the basic action and return values but lacks any usage guidance or behavioral details beyond creation. Given the moderate complexity (5 params, no annotations), this is a minimal but not comprehensive description.

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 80%, and the schema already describes most parameters. The description only reinforces that HTML is the input source, adding little beyond the schema. The type enum and project_id are left to schema documentation.

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 the specific verb 'Create' with the resource 'landing page' and the source 'from HTML', clearly distinguishing it from other maasy creation tools. It also states the output (landing_id and editor_url), making the purpose 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?

The description gives no guidance on when to use this tool versus alternatives, and no mention of prerequisites or exclusions. It simply states what the tool does without contextualizing it among the 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 provided, the description carries the full burden. It goes beyond a simple 'create' statement by disclosing the auto-generation of quick action pills and the loading behavior based on the user's tool. It omits details like permissions or error handling, but the disclosed behaviors are meaningful and helpful.

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 main verb and resource. Every sentence adds value: the first states the purpose, the second elaborates on content and behavioral features. No redundant 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 create tool with 7 parameters and no output schema, the description covers the purpose, content expectations, and a key behavior (action pills). It doesn't mention return values or prerequisites, but the schema and sibling context fill in some gaps. Overall, 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?

The input schema already has high coverage (86%), with descriptions for most parameters. The description adds some context about the content parameter ('actionable marketing knowledge in markdown') but does not clarify undocumented parameters like priority and max_tokens. This is consistent with a baseline of 3 when schema coverage is high.

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 'Create a new knowledge skill for maasy' with a specific verb and resource. It distinguishes from sibling tools like maasy_update_skill and maasy_list_skills by focusing on creation and adds relevant context about markdown content and action pills.

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: use this tool to create a new skill, with guidance on the type of content to include ('actionable marketing knowledge in markdown — frameworks, checklists, strategies'). It does not explicitly mention when not to use it or point to alternatives, but the sibling tool names make the context 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 full burden. It explicitly discloses the destructive, irreversible nature with "Permanently delete" and clarifies the effect: "Removes the knowledge from maasy." This is key behavioral context beyond the tool name, though it omits side effects or response behavior.

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 short, front-loaded sentences. The first states the action, the second reinforces the purpose. No wasted words or redundant information.

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

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

For a simple one-parameter delete tool, the description covers purpose and permanence. However, with no output schema and no return-value note, the agent may be uncertain about the response format or success indicators. Slightly more context on post-deletion behavior 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% with skill_id described as "Skill UUID." The description adds no extra parameter meaning, but since the schema fully documents the only parameter, 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 verb and resource: "Permanently delete a skill." It distinguishes from sibling tools like create, update, get, and list by specifying the delete action, making it 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 no guidance on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or relationships to create/update operations, leaving the agent without context on selecting delete over update or deactivation.

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 mentions output types but does not state whether this is a read-only operation, any permissions required, rate limits, or side effects. For a tool performing 'discovery', this is incomplete.

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

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

The description is a single concise sentence with no wasted words, effectively front-loading the tool's core purpose. However, it is borderline terse, offering minimal elaboration that could helpfully fill gaps.

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 relative simplicity (2 params, no output schema), the description provides basic output information but lacks context about return format, limitations, or how to interpret the results. It is adequate but leaves room for improvement.

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 no extra meaning beyond the schema's parameter definitions (project_id as 'Brand UUID' and seed_topic as 'Focus topic'), providing no additional context or usage 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 clearly states the tool performs AI keyword discovery and lists specific outputs (clusters, volume estimates, difficulty scores). This distinguishes it from sibling tools like maasy_get_seo_status or maasy_generate_content, though it lacks a strong action verb+resource structure.

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 is provided on when to use this tool versus alternatives, nor are any prerequisites mentioned. The description only states what the tool does, leaving the agent to infer usage context from the name alone.

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 of behavioral disclosure. It only says 'Execute,' without indicating side effects, reversibility, permissions, or what happens when the action runs. Since several actions (e.g., redistribute_budget, send_followup_leads) likely mutate state, this is a significant 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, front-loaded sentence with concrete examples. Every word earns its place; 'etc.' is a reasonable placeholder for the remaining enum values. 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?

A polymorphic executor with 8 possible actions and no output schema requires more context. The description does not explain what happens after execution (return values, success/failure), whether actions are reversible, or whether different actions have different requirements. Given the tool's complexity, the description is too thin.

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 50%: 'action' has an enum, which self-documents its values, and 'project_id' is described as 'Brand UUID.' The description adds examples of action values but does not explain the role of project_id or how it relates to execution. Overall, the schema carries the weight, with only marginal added meaning.

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 with a specific verb ('Execute') and resource ('copilot action'), and lists several concrete examples (generate report, redistribute budget, rotate creatives, diagnose campaigns). This differentiates it from the sibling tools, which are mostly getters/listers/generators. The trailing 'etc.' is acceptable, though it could have directly referenced the full enum.

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?

There is no guidance on when to use this tool versus alternatives. The description does not mention prerequisites, when it is preferred over other tools, or any exclusions. For example, it does not say whether to use this for one-off actions while other tools provide more focused operations.

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 for behavioral disclosure. It does not mention whether the tool has side effects, requires permissions, or what the output format is. 'Generate' suggests a non-destructive action, but this is not explicitly 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 single, front-loaded sentence with no filler. It efficiently conveys the tool's core function and key inputs (brand DNA, brief) without redundancy.

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

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

The tool has no output schema, so the description should explain what the tool returns. It only says 'Generate ad creatives', which is vague about the format (e.g., text, structured object, list). Behavioral aspects like whether the tool persists anything are also missing, making the description incomplete for a 4-parameter generation 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 schema already documents brief and project_id clearly. The description adds the concept of 'brand DNA', which loosely maps to project_id, but does not clarify count or platform semantics beyond the schema's enum and defaults. With 50% schema coverage, the description adds a small amount of value but does not fully compensate for missing parameter 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 generates ad creatives (copy + concepts) for Meta or Google using brand DNA and a brief. It specifies the exact deliverable and platforms, distinguishing it from sibling tools like maasy_generate_content, which is broader in scope.

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

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

The description provides no explicit guidance on when to use this tool versus alternatives. It simply describes what the tool does without mentioning context, exclusions, or alternative tools, leaving the agent to infer 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?

With no annotations, the description carries the full burden of disclosing behavior. It mentions 'Respects brand DNA, tone, ICP' which gives a hint of behavioral traits, but it doesn't explain the generation process, required inputs (like project_id), or return format. This is insufficient for understanding side effects or prerequisites.

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 very concise at two sentences, with no redundant information. The second sentence adds a behavioral trait but is still brief. It gets to the point efficiently, though it could be slightly more informative without losing 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?

Given the tool has three parameters, no output schema, and no annotations, the description is too minimal. It doesn't mention any prerequisites, what the output looks like, how platform affects generation, or error conditions. For a content generation tool, this is below the minimum viable description.

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 covers 67% of parameters with descriptions (prompt and project_id). The description adds no direct parameter semantics, but the schema already provides meaningful descriptions. The platform parameter has an enum but no description, and the description doesn't clarify it. Baseline 3 given the 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 generates on-brand social content via 'Generate on-brand social content using maasy AI.' It identifies the verb (generate), resource (social content), and a key attribute (on-brand). However, it does not explicitly distinguish it from the sibling maasy_generate_ads, which also generates content.

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 is provided on when to use this tool vs alternatives like maasy_generate_ads. The description only states what it does and that it respects brand DNA, but does not specify contexts, prerequisites, or 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, the description carries the full burden. It mentions 'pending' as a filter and lists alert categories, but does not disclose whether the tool is read-only, what the response structure looks like, or any side effects. This is a significant gap for a data-retrieval tool.

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

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

The description is a single, information-dense phrase that front-loads the core purpose ('Pending copilot alerts') and then provides the alert categories. Every word earns its place, and there is no 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?

Given the low complexity (2 optional parameters, no output schema), the description gives a minimal but usable sense of what the tool returns. However, it lacks usage context, parameter details, and any indication of the output format, leaving the agent to infer too much.

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 description adds no information about the 'limit' or 'project_id' parameters beyond the schema. While project_id has a schema description ('Brand UUID'), limit is only given a default, and the tool description does not explain how these parameters affect results.

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 pending copilot alerts and lists the types of alerts (campaign anomalies, lead opportunities, content gaps, SEO drops). This distinguishes it from sibling tools, none of which are alert-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?

No guidance is provided on when to use this tool versus alternatives, such as maasy_get_daily_summary or maasy_get_content_pipeline. The description merely implies usage when alerts are needed but does not state prerequisites or 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 burden of behavioral disclosure. It details what the tool returns (brand DNA fields) and implies a read-only operation, but does not go beyond that to mention potential errors, auth requirements, or rate limits. The listing of returned content adds some value, but safety and edge-case behaviors are not addressed.

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-sentence description that is front-loaded with the core definition, then a quick use-case note. Every word adds value, and the list structure is highly scannable.

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 a single parameter and no output schema, the description adequately covers what the tool returns and why it matters. It could mention return format or error scenarios, but given the simplicity, the current level is sufficient.

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

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

The input schema has 100% description coverage, with project_id clearly defined as 'Brand UUID'. The description does not add any additional parameter semantics, so the baseline score of 3 applies. The description's mention of 'brand' implicitly relates to the parameter, but no extra detail is provided.

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 the verb 'get' with the resource 'brand context' and specifies the full scope with a detailed list (name, industry, tone, ICP, value prop, assets, references). It clearly distinguishes itself from sibling tools like list_brands by emphasizing 'Full brand DNA' and its unique role in on-brand generation.

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 'Essential for on-brand generation' provides clear context for when to use this tool. However, it does not explicitly mention alternatives or exclude other tools, though the context implies it is the go-to for complete brand data before content creation.

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 present, so the description bears the responsibility for behavioral transparency. It lists the returned metrics, which imply a read-only data retrieval, but it does not explicitly state that the operation is safe, requires no side effects, or any constraints. The description is not contradictory but is minimal in behavioral 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, compact line that immediately communicates the tool's focus and enumerates all core metrics. It contains no filler words and is well-structured for quick parsing.

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 simple (two optional parameters, no output schema), and the description names the key output metrics, which are standard terms (CTR, CPC, ROAS) that an agent would recognize. It does not describe the output format or units, but the context is sufficiently covered for a straightforward metrics retrieval tool.

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

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

Both parameters (days, project_id) are fully described in the schema (100% coverage), so the baseline is 3. The description does not add any additional meaning or usage context for these parameters 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 identifies the specific resource (ad campaign) and the key performance metrics (spend, impressions, clicks, etc.), which clearly differentiates it from sibling tools like CRM summary or SEO status. However, it uses a noun-phrase format without an explicit verb, so the action is only implied by the tool name.

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 the tool is used when ad campaign performance data is needed, but it does not explicitly state when to use it versus alternatives like maasy_get_crm_summary or maasy_get_daily_summary. No exclusions or alternative tool references are provided.

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, and it does disclose key filtering behavior (statuses and time window). However, it does not state read-only nature, return format, or how project_id scoping behaves, so transparency is partial.

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 fragment with no filler and front-loads the resource name. It is concise but omits a verb and contextual qualifiers, so it falls just short of maximally effective.

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

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

Without output schema or annotations, the agent needs more context about what the tool returns and how the parameters interplay, but the description only gives a terse state list. The tool is simple, yet the description still feels under-specified for selection and invocation.

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

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

The schema covers project_id with 'Brand UUID,' and the description's 'last N days' adds meaning to the days parameter, which is otherwise only a number with a default. Still, descriptive links between parameters and behavior are minimal, leaving some interpretation to the agent.

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 resource as 'content pipeline' and specifies its scope with 'drafts, scheduled, published, by status, last N days,' which distinguishes it from sibling get_* tools. It lacks an explicit verb, so it reads as a noun phrase rather than a directive, but the intent to retrieve pipeline content is clear.

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 usage context is provided; the description does not state when to choose this tool over alternatives like maasy_get_daily_summary or maasy_get_campaign_metrics. No exclusions or prerequisites 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?

No annotations are present, so the description carries the full burden of behavioral disclosure. It lists the returned contents but does not state whether the operation is read-only, what permissions are required, whether the data is aggregated, or what happens when the optional project_id is omitted. This leaves the agent with significant uncertainty about side effects and data scope.

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 brief, consisting of a single line with a colon-separated list. It is concise with no filler, which is good, but it lacks a complete sentence structure, making it somewhat abrupt. It earns high marks for brevity but not for structural completeness.

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 one optional parameter and no output schema, the description provides a basic inventory of return data. However, it lacks context about the optional parameter, data characteristics (e.g., time range, currency), and relationship to sibling tools, leaving gaps that an agent must infer. This is minimally viable but not fully 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%: the only parameter project_id is described as 'Brand UUID'. The description adds no additional meaning beyond the schema, such as the effect of omitting it (the schema marks it optional) or whether it scopes the summary to a specific brand. Baseline 3 applies because the schema already documents the 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 enumerates the CRM summary contents (leads by status, hot leads, contacts, opportunities, total pipeline value), clearly identifying the resource and data provided. The verb 'get' in the tool name confirms this is a retrieval operation. However, the description is a noun phrase rather than a full sentence and does not explicitly differentiate from sibling summary tools beyond the 'CRM' qualifier.

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 is provided on when to use this tool versus the many sibling tools (e.g., get_content_pipeline, get_campaign_metrics, get_daily_summary). It does not mention prerequisites, which project context is needed, or scenarios where this CRM summary would be preferred over alternatives.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does not state whether the operation is read-only, what happens if project_id is omitted, whether results are cached, or what the response structure looks like. The content list (what happened, needs attention, actions) provides some semantic output expectation but insufficient behavioral 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 concise sentence that front-loads the core purpose ('Today's operations summary') and immediately follows with the key content categories. Every phrase earns its place; no redundancy or irrelevant information.

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

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

Given the tool has only one optional parameter, no output schema, and no annotations, the description provides a minimal but adequate outline of the returned summary. However, it lacks guidance on how the optional project_id is used, whether the summary is scoped to a brand, or any prerequisites. For a tool with many sibling summary tools, more contextual differentiation would be helpful.

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% coverage for the single parameter (project_id as 'Brand UUID'), so the description does not need to repeat it. However, the description also adds no additional meaning about how project_id affects the summary (e.g., filtering by brand, required vs optional), so it neither enhances nor compensates beyond the schema 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 indicates this tool provides a daily operations summary with three specific content areas: what happened, what needs attention, and recommended actions. It uses the resource 'daily summary' which is distinct from sibling tools like get_crm_summary or get_content_pipeline, but it does not explicitly name or differentiate from those 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?

No guidance is provided on when to use this tool versus other summary tools like get_campaign_metrics or get_alerts. There is no mention of prerequisites, intended use cases, or exclusions, leaving the agent to infer from the name alone.

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 of disclosing behavioral traits, but it only lists output categories without explicitly stating that this is a read-only operation, any side effects, or access requirements. The lack of a verb or behavioral context leaves important aspects undisclosed.

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 very short and front-loads key terms, but it is a sentence fragment without a verb. It sacrifices structural clarity for brevity, making it less useful than a complete sentence that states the action and resource.

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 absence of annotations and an output schema, the description is the only source of behavioral context. It lists metrics but does not explain their meaning, the return format, or how project_id affects the results. This is insufficient for an agent to fully understand the tool's behavior.

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 provides 100% coverage for the single parameter 'project_id' with the description 'Brand UUID'. The tool description does not mention this parameter or add any additional semantics, so it adds no value beyond the schema. 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 is a noun phrase listing data categories ('SEO/GEO scores, keyword rankings, visibility trends, top queries') rather than a sentence with an explicit verb or resource. The tool name 'get_seo_status' implies retrieval, and the listed terms distinguish it from sibling tools, but the description itself does not clearly state what it does.

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 no guidance on when to use this tool versus alternatives like maasy_get_campaign_metrics or maasy_get_content_pipeline. There is no mention of context, prerequisites, or exclusions, leaving the agent to infer usage solely from the tool name and metric list.

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 the key behavioral aspect of the return value: it includes content and auto-generated quick action pills. As a 'get' operation, the read-only nature is self-evident, and unlike a mutation, there are no additional side effects to disclose. This adds practical context beyond the minimal 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 a single, concise sentence that front-loads the action and key details. Every word earns its place, 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?

For a simple read operation with one parameter and no output schema, the description sufficiently explains what the tool does and what the response will include (content, pills). It could mention return format or error handling, but these are minor gaps for a basic get 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 schema already documents skill_id as 'Skill UUID' with 100% coverage. The description does not elaborate on the parameter beyond implicitly referring to the skill, so it provides no additional semantic value.

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 with a specific verb ('Get') and resource ('skill'), and differentiates it from siblings like maasy_list_skills by specifying 'full details' including 'content and auto-generated quick action pills'.

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 usage is implied: this is the tool to retrieve full skill details, as opposed to listing skills or executing actions. However, there is no explicit guidance on when to prefer this over alternatives or any 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 burden. It confirms it lists all brands and returns basic info, but it does not elaborate on what 'basic info' includes, potential pagination, or any other behavioral characteristics. This is adequate for a simple list tool but lacks depth.

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 is front-loaded with the action and resource. It contains no fluff or redundant information.

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

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

The tool is simple with no output schema, but the description is vague about what 'basic info' entails. Since there are sibling tools that provide more detail (e.g., maasy_get_brand_context), the description could have pointed to them, but it doesn't. Adequate for a basic list but leaves some 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?

The tool has zero parameters, and the description's phrase 'all brands' implies no filtering is needed. With no parameters, the baseline is 4, and the description aligns well without needing to explain any parameter 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 clearly states the tool lists all brands (projects) in the maasy account with basic info. The verb 'List' and resource 'brands' are specific, and it distinguishes from siblings like maasy_get_brand_context (which targets a specific brand) and maasy_list_skills (which lists a different entity).

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 context for use is clear: when you need an overview of all brands with basic info. However, it does not mention alternatives or explicitly state when not to use this tool, though sibling contrast is implied by the tool name and description.

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 responsibility for behavioral disclosure. It merely says 'List all maasy copilot skills' without revealing optional category filtering, the active_only default, or the return format. The word 'all' is potentially misleading given the filter.

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, front-loaded with the action, with no filler words. It is appropriately sized for a simple list operation.

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 a simple tool, the description omits essential context: the optional category filter, the active_only flag, and what the response contains (no output schema present). An agent would not know it can filter results or that active_only defaults to true, making the tool less discoverable.

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 0%, so the description must compensate. It gives examples of categories ('SEO, ads, CRM playbooks') that loosely map to enum values, but it does not explain the active_only parameter or how the category filter behaves. Partial compensation at best.

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 operation ('List all maasy copilot skills') and defines the resource type as modular knowledge packages. It distinguishes from sibling tools like get_skill (singular retrieval) and create/update/delete operations.

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 versus alternatives. It does not mention maasy_get_skill for fetching a single skill or provide context such as 'use to browse available skills.' The intended usage is only implied by the verb 'List.'

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 does reveal the output format ('traffic-light status per area'), but it does not explicitly state whether the scan is read-only, whether it triggers side effects, or if any permissions are needed. The term 'scan' suggests non-destructive behavior, but that is not explicit.

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 well-structured sentence that front-loads the core purpose ('Deep health scan'), uses a colon to introduce the list of areas, and ends with the output specification. No redundant or filler 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?

Without an output schema, the description sufficiently explains the return value ('traffic-light status per area') and enumerates all scanned areas. However, it could be more explicit about what 'traffic-light status' means (e.g., red/yellow/green) or how to interpret the results, and it does not hint at how to drill down for details via other tools. Overall, it is largely complete for a holistic health scan 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 input schema provides 100% coverage for the sole parameter 'project_id' with a clear description ('Brand UUID (uses default if omitted)'). The tool description adds no parameter information. Given the high schema coverage, a baseline score of 3 is appropriate.

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

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

The description clearly states a specific action ('deep health scan'), the resource ('brand'), and enumerates the areas covered (DNA completeness, campaigns, content, CRM, SEO, alerts). It also specifies the output ('traffic-light status per area'), which distinguishes it from sibling tools that focus on individual areas like maasy_get_seo_status or maasy_get_alerts.

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 a holistic overview use case by listing multiple areas to scan, but it does not explicitly state when to use this tool versus the more specific sibling tools, nor does it mention when not to use it. No alternatives are referenced, so the guidance is only implicit.

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 of behavioral disclosure. It states that it schedules a post and returns post_id and scheduled time, but it does not disclose that omitting scheduled_at saves the post as a draft, nor does it mention any side effects, permissions, or reversibility. The description is too sparse to fully convey the tool's behavior.

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, well-structured sentence that communicates the core function and return value without unnecessary words. It is front-loaded with the action, making it easy for an agent to quickly grasp the tool's purpose.

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

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

For a tool with 6 parameters, no output schema, and no annotations, the description is too brief. It omits key behavioral context such as the draft/scheduled auto-detection, the default content_type, and the relationship between status and scheduled_at. This leaves significant gaps for an agent trying to invoke the tool correctly, especially given the tool's 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 description coverage is 83% (5 of 6 parameters have descriptions), which is high. The description itself does not elaborate on any parameters, so it relies on the schema. The mention of 'scheduled time' aligns with scheduled_at but adds no new detail beyond the schema. Baseline 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 uses a specific verb 'Schedule' with a clear resource 'a social media post in maasy', and mentions the return values (post_id and scheduled time). This distinguishes it from sibling tools like maasy_generate_content or maasy_create_landing, which have different purposes. The purpose is 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 this tool is for scheduling posts, and the context of siblings suggests it is the correct choice for posting/scheduling actions. However, it does not explicitly state when to use this tool over alternatives, nor does it mention the draft vs. scheduled distinction that the schema hints at. Usage is inferred rather than directly guided.

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 of behavioral disclosure. It only says 'manually trigger' and lists workflow types, but does not explain side effects, whether execution is synchronous/asynchronous, any required permissions, or what happens after triggering. This is insufficient for a mutation-like action.

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, front-loaded sentence with no wasted words. It is concise, though it could be slightly more informative without losing brevity. The structure is simple and readable.

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, one nested object, no output schema, and no annotations, the description is too sparse. It does not explain return values, asynchronous behavior, or operational context, leaving the agent without a full picture of the tool's behavior.

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 all four parameters described in the schema. The description adds no parameter-specific details beyond the schema, so it meets the baseline but does not improve comprehension.

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 triggers a maasy workflow manually, with a specific verb ('Trigger') and resource ('maasy workflow'). It mentions the workflow types (automations, sequences, CRM flows), providing some context. However, it does not explicitly distinguish it from sibling tools like maasy_execute_action, so it does not fully differentiate.

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?

There is no guidance on when to use this tool versus alternatives. No mention of prerequisites, exclusions, or scenarios where other tools would be more appropriate. The description lacks any comparative or contextual usage hints.

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 a specific side effect: 'Regenerates quick action pills if content changes.' However, it does not mention return values, error behavior, or whether updates are partial or full, leaving notable gaps.

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

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

The description is two concise sentences. The first sentence states the action and fields, and the second adds the key side effect. No wasted words; 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?

With 8 parameters, no output schema, and no annotations, the description omits essential context such as what the tool returns, how partial updates are handled, and any error conditions. The side-effect mention is helpful but insufficient for a tool with 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?

Schema description coverage is only 13% (only skill_id has a description). The description names four fields (content, category, priority, active status) but omits name, max_tokens, and description, and does not clarify semantics like priority range or whether null values unset fields. It adds little 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 verb 'update' and the resource 'skill', and specifies the modifiable fields: content, category, priority, or active status. This distinguishes it from sibling create/delete tools, 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?

Usage context is implied by the word 'update' and the resource 'skill', but the description does not explicitly say when to prefer this over create/delete or other sibling tools. It provides clear context (modifying existing skills) but lacks explicit exclusions or alternative guidance.

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