Cadencz

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

No annotations are present, so the description must carry the behavioral burden. It implies a state change from 'plan item' to 'scheduled post' but does not disclose whether approval is reversible, what side effects occur, whether permissions are needed, or what the response contains.

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, direct sentence with no filler or redundant information. However, it is somewhat terse and leaves several important details unstated.

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 one-parameter tool, the description conveys the core operation and the object being acted on. But with no annotations and no output schema, the lack of behavioral detail and missing explanation of item_id prevents the description from being fully self-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 schema provides only the parameter name and type with no description, and schema description coverage is 0%. The phrase 'plan item' indirectly suggests that item_id identifies a plan item, but the description never explicitly ties item_id to that entity or explains how to obtain the 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 uses a specific verb, 'Approve', with a clear resource, 'plan item', and an outcome, 'into a scheduled post'. This distinguishes it from siblings like schedule_post and publish_now, though 'plan item' is not fully defined.

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 about when to use this tool versus alternatives such as schedule_post, publish_now, or cancel_scheduled_post. The intended workflow or preconditions for approval are left entirely 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?

With no annotations, the description carries the full behavioral burden. It does disclose the primary effect — moving a scheduled variant back to draft — which is meaningful. However, it does not clarify whether the action is reversible, whether it removes the post from any scheduling queue, or what happens to related data.

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. Every word contributes to understanding the action and target, making it an excellent model of 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?

For a simple one-parameter mutation, the description covers the core behavior, but it omits helpful context like how to identify the variant (e.g., via list_scheduled_posts) or edge cases (e.g., already published variants). The sibling tool list suggests some context, but the description itself is somewhat bare.

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 0%, so the description must compensate. The term 'scheduled variant' provides some semantic connection to the required variant_id parameter, implying the ID identifies the scheduled variant. But it does not specify how to obtain the ID or any format expectations, leaving room for ambiguity.

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 ('Pull') with a clear resource ('a scheduled variant') and outcome ('back to draft'). This makes the tool's core function clear and distinguishes it from siblings like schedule_post or publish_now, though it does not explicitly name 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?

The description gives no explicit guidance on when to use this tool versus alternatives. It implies the use case (undoing a scheduled post), but does not state what preconditions apply (e.g., variant must still be scheduled, not already published) or mention related tools like list_scheduled_posts.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It does disclose that the tool draws one AI generation and grounds the post in a Brain, which are useful behavioral traits. But it does not say whether the post is saved, published, pending approval, or what permissions or irreversible effects are involved.

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 compact sentences with no filler. It front-loads the core action, then adds context about grounding and the AI generation cost, so every sentence earns its place.

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 annotations or an output schema, the description leaves important invocation context unspecified: what the tool returns, whether the composed post is immediately published or merely drafted, and how the platform parameter behaves. For a state-changing generative tool, this is a meaningful completeness gap.

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 explains 'idea' as the source of the post and clarifies that 'brain_id' overrides the default workspace Brain. The 'platform-native' wording hints at the platform parameter but does not explain possible values or how the platform is selected, leaving partial parameter 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 a specific action ('Write one platform-native post from an idea') and resource (a post), and adds meaningful context about being grounded in the Brain. It does not explicitly contrast itself with siblings like publish_now or schedule_post, so it stops short of full sibling differentiation.

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

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

The description implies when to use the tool: when the user has an idea and wants an AI-generated post grounded in a Brain. However, it provides no explicit when-not-to-use guidance and does not name alternatives, so the usage context is implied rather than stated.

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 what is returned, not whether the operation is read-only, what the default window is, how data is aggregated, or any other behavioral traits. The name implies retrieval but the description does not substantiate it.

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 short sentence with no filler and the key phrase 'Engagement totals per platform' is front-loaded. It loses a point because it is a noun fragment rather than a complete actionable sentence, though it remains appropriately 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?

This is a simple tool with one optional parameter, but there is no output schema and the description is minimal. It does not convey the expected result shape, the default time window, or any usage details, leaving an agent under-informed to call it reliably.

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. The phrase 'over a window' loosely relates to the `days` parameter, but it does not explicitly explain that `days` defines the window length, what the default is, or how to choose it. This is insufficient for an agent to understand the parameter's 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 identifies the resource and output: engagement totals per platform over a window. It is specific enough to distinguish from sibling tools, none of which focus on analytics. The lack of an explicit verb is minor because the tool name 'get' supplies the action.

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 context for appropriate invocation, and no mention of any exclusions. The description simply states what the tool returns, leaving the agent to infer when it should be called.

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 states what the tool returns and does not explicitly confirm a read-only operation, explain side effects, or describe response behavior. The 'get' prefix implies non-destructive use, but the description itself does not disclose this.

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

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

The description is a single short sentence with no filler or redundant phrasing. Every word contributes to identifying the returned resource, and the key concept 'current weekly content plan' appears first. It is appropriately minimal for a no-parameter getter.

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 no-parameter tool, the description gives the essential return value, but it omits explicit read-only confirmation and does not differentiate usage from related siblings like get_plan_usage. Without an output schema or annotations, a slightly fuller description would make the tool more self-contained. It is adequate but has clear gaps.

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 is empty, so there are no parameter semantics to clarify. The baseline for zero parameters is 4, and the description adds no conflicting or missing parameter information. Nothing further is needed in this dimension.

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 resource as the current weekly content plan and adds that it includes items, so an agent knows what the tool returns. It does not use an explicit verb, but the tool name 'get_current_plan' supplies the action. It is distinguishable from siblings like get_plan_usage and plan_week, though not by explicitly contrasting them.

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 explicit guidance about when to use this tool versus alternatives. There is no mention of a scenario, prerequisite, or exclusion, and an agent must infer from the name that this is for retrieving the current plan. Sibling tools such as get_plan_usage and plan_week are not referenced.

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 behavioral burden. It conveys that the tool reports allowance usage against caps for the period, which implies a read-only query. However, it doesn't explicitly state return format, side-effect profile, or how 'this period' is defined.

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?

A single, front-loaded sentence conveys the essential subject, comparison object, and time scope with no filler. Every word contributes meaning.

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 no-input, no-output-schema read tool, the description is reasonably complete: it names the quantity (allowance usage), the reference (plan caps), and the window (this period). It leaves 'the plan' and 'this period' slightly implicit, but sibling context makes these understandable.

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

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

There are zero parameters, so there is nothing for the description to clarify beyond the empty input schema. The 100% schema coverage plus no parameters makes the baseline of 4 appropriate.

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

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

The description clearly identifies the specific resource: allowance usage compared against the plan's caps for the current period. It distinguishes get_plan_usage from get_current_plan by focusing on usage/caps rather than plan details, though it does not explicitly name any sibling.

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 the agent needs to know how much allowance has been consumed against plan caps this period. It does not provide explicit when-to-use guidance or state when a sibling like get_current_plan or get_analytics would be more appropriate.

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

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

No annotations are provided, so the description carries the full behavioral burden. It only states 'The Website Brains' without disclosing that the operation is read-only, returns a collection, or any edge-case behavior. There is no contradiction, but the description adds little beyond a noun phrase.

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 short, but it reads as a sentence fragment rather than a complete, informative sentence. It is under-specific rather than appropriately concise, similar to a description like 'Process' – every word is short but the message is incomplete.

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 and no annotations, the description should at least clearly state that the tool returns the list of website brains and what those include. It doesn't define 'Brain', whether the result is an array, or any relevant details. For a zero-parameter tool, leaving out such basics is unencessarily incomplete.

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 input schema is an empty object, so nothing needs to be described. The absence of parameters makes this dimension trivially satisfied; the baseline of 4 applies.

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

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

The description names the resource ('Website Brains') and the scope ('this workspace'), but lacks an explicit verb such as 'list' or 'retrieve'. The tool name supplies the action, making it semi-redundant, yet it does convey the target resource. It does not differentiate the tool from sibling list tools other than by the resource 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?

No guidance is given about when to use this tool versus sibling tools like query_brain, list_workspaces, or list_channels. The only contextual hint is the workspace scope, which is thin and leaves routing to inference.

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 supplied, so the description carries the disclosure burden. It does reveal the primary behavior: the tool reports connected social accounts and whether the plan covers each. It doesn't cover edge cases, auth, or output format, but for a zero-parameter read-only listing tool, this is minimally adequate.

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 text is just one clause with no filler, and the core subject is front-loaded. It is slightly underspecified because it is a noun phrase rather than a full sentence, but it wastes no tokens.

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 parameterless tool with no output schema, the description gives enough to predict the returned data: channels and plan coverage. It leaves 'the plan' underspecified and gives no relationship to get_current_plan or get_plan_usage, so it is not fully self-contained.

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

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

The input schema has no properties and no required parameters, so there are no parameter semantics for the description to add. The 100% schema coverage is vacuous, and the baseline for zero-parameter tools is 4.

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

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

The description identifies the tool's subject (connected social accounts) and the key result (whether the plan covers them), so an agent can infer it lists channels with plan-coverage status. It lacks an explicit action verb and doesn't directly contrast with sibling tools, but 'channels' is a distinct resource among the list_* siblings.

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

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

There is no statement of when to use this tool instead of get_current_plan, get_plan_usage, or list_workspaces. No exclusions, prerequisites, or context cues are given; the only signal is the tool name itself.

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?

There are no annotations, so the description carries the full burden. It gives some context by saying the posts are 'committed to publish,' but it does not disclose whether the operation is read-only, what the output contains, how the days parameter affects results, or any ordering or filtering 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 concise sentence with no filler. It is front-loaded with the core resource, though it is phrased as a noun phrase rather than an actionable verb phrase.

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 list tool with one optional parameter, the description is minimally viable but leaves gaps: no explicit read-only guarantee, no default behavior, no output shape, and no distinction from related scheduling tools. It is adequate but not 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 single optional days parameter has no schema description, and the description's 'coming days' phrase loosely maps to a future-time horizon. This provides partial meaning but does not clearly explain that days controls how far ahead to look or what happens when it is omitted.

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 posts committed to publish in the coming days, which combines with the tool name to clearly indicate a listing operation. It is not an explicit verb phrase, but it is unambiguous enough to separate from scheduling or canceling tools.

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

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

No guidance is given about when to use this tool versus sibling tools like schedule_post, cancel_scheduled_post, or publish_now. The intended context is only 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, the description carries the full behavioral burden. It does reveal that results are limited to what the current token can see, implying a token-scoped read-only operation, but it does not mention pagination, error behavior, or response structure.

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 phrase with no wasted words. It is front-loaded with the essential information: the resource and its scope.

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

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

For a zero-parameter, simple list tool with no output schema, this description is largely sufficient for invocation and selection. It lacks explicit return-field details, but that is a minor gap given the tool's simplicity.

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 accepts no parameters, so there is no parameter documentation burden on the description. The zero-parameter baseline of 4 applies here.

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 resource (workspaces) and the token-scoped visibility, which distinguishes it from sibling tools like list_brains and list_channels. However, it lacks an explicit verb like 'list' or 'return', so purpose clarity is strong but not perfect.

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 about when to use this tool versus alternatives such as list_brains or list_channels. The description gives no context about selection criteria, exclusions, or alternative 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?

Annotations are absent, so the description carries the behavioral burden. It does disclose one important consequential trait: planned posts consume the shared AI-generation allowance. However, it does not state whether running the tool mutates existing plans, creates drafts for approval, or produces a visible result.

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 sentences, with the command front-loaded and the key consequence second. Every word earns its place; there is no filler or redundancy.

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

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

For an action tool with no annotations and no output schema, the description does not say what happens after invocation beyond consuming allowance. An agent can call it with zero parameters, but it cannot tell whether a plan is generated for approval, whether schedules are changed, or what state results. The description is too thin for a side-effecting workflow operation.

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

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

The tool has zero parameters and the input schema already documents this with an empty properties object. No parameter-level documentation is needed, so the rubric baseline of 4 applies.

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

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

The description names a specific action ('Run Plan My Week') and a concrete consequence ('planned post'), making it clear this is a batch planning operation. It does not explicitly contrast with siblings like schedule_post or get_current_plan, so some differentiation must be inferred from naming and 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?

There is no guidance about when to use this tool versus alternatives, no exclusions, and no mention of prerequisites. The sibling list implies a planning workflow, but the description does not direct an agent to choose this over schedule_post or approve_plan_item. It relies entirely on the tool name.

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 explicitly warns that the operation is 'Irreversible' and demands 'confirm: true,' which are critical guardrails for a mutating action. It doesn't elaborate on what 'queue' means operationally (e.g., when delivery happens), but the irreversibility warning is strong and useful.

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 one compact sentence that packs purpose, behavior, and a usage constraint into minimal words. Every phrase earns its place, and the core intent 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?

For a two-parameter tool with no output schema, the description is mostly adequate: it states the operation, the irreversibility, and the required confirm flag. It lacks guidance on failure cases, return values, or alternative routing to schedule_post, but an agent can likely invoke it correctly with the given information.

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 for the parameters. It only mentions that confirm must be true, which adds a small semantic constraint, but variant_id is completely unspecified. This does not meaningfully help an agent understand what identifiers are valid or how the parameters interact.

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 specifies a concrete verb+resource: 'Queue a variant for immediate delivery,' which clearly distinguishes it from planning or scheduling tools like plan_week and schedule_post. The 'immediate delivery' scope adds useful precision. It doesn't explicitly name a sibling, so it loses one point on differentiation.

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?

'Immediate delivery' clearly signals the use case: publish now rather than schedule later. 'requires confirm: true' states a mandatory precondition for invocation. However, it does not explicitly state when not to use it or name an alternative tool such as schedule_post, so it's a 4 rather than 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?

No annotations are provided, so the description carries the full burden. It does disclose a useful non-obvious behavior: omitting brain_id defaults to the workspace's Brain. But it does not disclose output shape, failure behavior, or whether the operation is read-only beyond the word 'retrieve'.

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 entire description is one efficient sentence. It front-loads the core action and then adds the key behavioral detail about the default brain. No filler or redundancy.

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

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

For a simple two-parameter query tool, the description is nearly sufficient: it states the action, the input, and the brain-selection default. Since no output schema exists, a note on the returned product-context structure would help, but the low parameter complexity makes this a minor gap.

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 clarifies that 'question' is the query and 'brain_id' is optional with a workspace fallback. However, it gives no format, type hints, or examples, so it only partially compensates.

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?

Uses a specific verb ('Retrieve') with a clear resource ('a Brain') and purpose ('for a question'). It clearly distinguishes itself from siblings like list_brains, which list brains rather than retrieve context from a specific brain.

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

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

The description implies usage context by explaining the workspace-brain default when brain_id is omitted. However, it does not explicitly state when to prefer this tool over alternatives like list_brains, or when this tool would not be appropriate.

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

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

With no annotations, the description carries the full burden of behavioral disclosure, but it only states the action without mentioning side effects, prerequisites, state changes, or reversibility. For a mutating tool, this leaves critical behavior opaque.

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 sentence with no wasted words, but it is so sparse that it borders on under-specification. It is concise in form yet lacks the operational detail needed for an agent to use the tool correctly.

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 3 parameters, no output schema, and no annotations, the description is incomplete. It omits timezone semantics, the lifecycle of the scheduled post, and how this relates to sibling tools such as publish_now or cancel_scheduled_post.

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 33%, so the description must compensate. Phrases like 'draft variant' and 'moment in time' map conceptually to variant_id and scheduled_at, adding some meaning, but the timezone parameter is entirely unexplained.

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') and identifies the resource ('draft variant') and temporal qualifier ('moment in time'), making the tool's basic function clear. It implicitly distinguishes itself from publish_now and cancel_scheduled_post, though it doesn't explicitly name an alternative.

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

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

The description provides no guidance on when to use this tool versus alternatives like publish_now or cancel_scheduled_post. There are no prerequisites, exclusions, or context hints to help an agent decide correctly.

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