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 meaningful behavior: two normalization modes, a deterministic growth gate, and the side effect of appending only to the current owner. This is more transparent than a typical one-line mutation description, though it omits permission requirements and failure 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 dense sentence with no filler or repetition. It front-loads the process in execution order and packs normalization options, a validation step, and scoping into a compact, easily parsed form.

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 4-parameter tool with no annotations and no output schema, the description is too terse. It does not explain sequence semantics, prerequisites, return behavior, or how this step fits into the broader growth lifecycle. An agent would need substantial domain knowledge to invoke this tool confidently.

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 only 25%, and the description only partially compensates. It clarifies the normalization options ('Tetrees Agent' vs 'provider-free portable normalization') and hints at owner scoping, but it never explains the required 'sequence' parameter or how the 'proposed delta' maps to input fields. This leaves significant ambiguity for 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 states a concrete operation: normalize a proposed delta, run a growth gate, and append it to the current owner. This makes clear the tool is about accepting/applying a growth delta for an AI Pack. However, it does not explicitly distinguish itself from sibling tools like select_ai_pack_growth_version or accept_ai_pack_terms.

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 'proposed delta' implies the tool is used after some growth proposal exists, and the confirmation constant suggests the proposal was reviewed. But there is no explicit guidance about when to use this tool versus propose_ai_pack_growth, select_ai_pack_growth_version, or accept_ai_pack_terms. No alternatives or exclusion conditions 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 provided, so the description carries the full burden of explaining side effects. 'Record' implies a persistent state change, but the description does not disclose whether the acceptance is irreversible, idempotent, account-wide, or what the response indicates after recording.

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 communicates the action, the resource, and the prerequisite conditions with no wasted words. The ordering is logical: what the tool does first, then when it is needed.

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 one const parameter and no output schema, so the description is mostly adequate for selection and invocation. However, because there are no annotations and no behavioral details about the recorded agreement, the agent is left to infer whether the call is repeatable and what success means.

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%, and the description never mentions the confirmation parameter or the exact required constant. The schema's const value is self-explanatory, but the description does not compensate for the missing parameter 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 states the verb 'Record' and the resource 'current account agreement', and adds the required-before context for creating, uploading, auditioning, or publishing a pack. It clearly identifies the tool's role, though it does not explicitly distinguish it from the similar-sounding sibling accept_ai_pack_growth.

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

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

The description explicitly says the agreement is required before creating, uploading, auditioning, or publishing a pack, giving an agent clear conditions for when to use it. It does not name alternatives or state when not to use it, so it stops short of full guidance.

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

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

With no annotations, the description carries the behavioral disclosure burden. It discloses that the result is a revocable entitlement, which addresses reversibility, and explicitly states that no Stripe/payment is involved. It could add details about idempotency or duplicate entitlements, but for a one-parameter tool the core behavior is well covered.

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 a single sentence that front-loads the action and object, then appends two key qualifiers: 'published free pack' and 'without Stripe'. Every phrase 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?

The tool is simple: one required parameter, no output schema, no annotations. The description explains the action, the target object, the entitlement's revocability, and the absence of payment. It does not mention edge cases like duplicate creation or revocation mechanics, but these are not essential for selecting and invoking this tool correctly.

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% and productId is documented as 'Tetrees AI Pack product id'. The description adds meaning by constraining productId to a 'published free pack', which tells the agent which kind of product identifier is valid. This goes beyond the schema's generic field description.

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-resource pair: 'Create a revocable entitlement' for a 'published free pack', and distinguishes this from payment flows by adding 'without Stripe'. This clearly separates it from sibling tools like upload, download, search, and publish.

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 usage context: this tool is for acquiring a free pack entitlement, not for paid purchases or unpublished packs. It does not explicitly name alternative tools or state when not to use it, but the context is specific enough for an agent to route correctly.

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 behavioral burden and does disclose meaningful traits: the result is owner-bound, it is a draft state, current terms must be accepted first, and free drafts skip Stripe Connect. It could add what happens after creation, but it goes well beyond restating the tool name.

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 three tight sentences with no filler: purpose and content requirements come first, then prerequisites and the Stripe exception. Every sentence adds distinct value.

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

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

For a tool with a large nested schema and no output schema, the description provides a reasonable high-level contract but leaves gaps: it does not explain how to construct the 'complete buyer guide' fields, what a successful response returns, or how the created draft relates to later sibling operations like update or submit. The schema compensates structurally, not semantically.

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 parameter meaning, but it only alludes to listing content ('complete buyer guide', 'two concrete example uses'). It does not clarify the semantics of name, category, capabilities, models, priceUsd, or the nested listing fields beyond what the raw JSON schema names imply.

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 a specific action ('Create') and resource ('owner-bound AI Pack listing'), and adds content qualifiers ('complete buyer guide', 'two concrete example uses') that distinguish this from a generic draft creation. It differentiates from the sibling update_ai_pack_draft through the 'create' framing.

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

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

It explicitly states the precondition 'Accept current terms first' and the condition 'Free drafts do not require Stripe Connect', giving the agent clear sequencing and payment-account context. It does not name alternative siblings for editing or submitting an existing draft, but the create-vs-update distinction is strong.

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, and it does disclose key behavior: it returns a short-lived, entitlement-gated URL rather than the file directly. This is meaningful context that helps the agent understand side effects and constraints.

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 tightly-worded sentence with no filler. Every qualifier—short-lived, entitlement-gated, signed .taip envelope—carries useful information and 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 tool with one required parameter and no output schema, the description gives the essential information: the result is a URL, it is short-lived, and entitlement is required. It is slightly terse about prerequisites or error conditions, but adequate for 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?

Schema description coverage is 100%, so the single productId parameter is already fully documented in the schema. The description adds no parameter-specific meaning beyond the schema, which matches the baseline for high 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: returning an entitlement-gated URL for the signed .taip envelope, and the title further clarifies it downloads an owned Tetrees AI Pack. This distinguishes it from sibling tools like list_owned_ai_packs or run_ai_pack.

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 explicit guidance is given for when to use this tool versus alternatives. The description implies downloading is the use case, but it does not mention conditions, exclusions, or how it relates to siblings such as list_owned_ai_packs or get_ai_pack_runtime_profile.

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 transparency burden and does disclose meaningful behavior: configuration and credentials stay local, and write methods require an exact one-call confirmation. It does not cover response behavior or failure modes, but the highest-risk aspects are communicated.

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

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

Two sentences, front-loaded with the action and resource, followed by security and confirmation constraints. Every sentence adds value and there is no redundant or filler content.

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 no annotations, low parameter coverage, and no output schema, the description provides a solid starting point but omits return value semantics, error/confirmation failure behavior, and how the three execution channels are chosen or configured. It is adequate but leaves several gaps for the agent to resolve.

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 40%, so the description needed to compensate for methodId, extensionId, and arguments. It partially does by connecting 'one method from an owned Pack' to the ID parameters, and it reinforces writeConfirmation behavior, but it does not explain the shape or role of arguments or how the execution channel is selected.

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

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

The description states a concrete action ('Execute one method from an owned Pack') and names three specific execution channels, making its purpose clear and distinct from broad pack-management tools. It does not explicitly contrast with run_ai_pack or run_ai_pack_audition, but the 'owned Pack' and 'client extension' scope provide enough 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 usage when the user owns an auditioned Pack and wants to invoke one method through a configured execution environment. It also notes the write-confirmation precondition, but it does not explicitly state when to prefer this tool over alternatives or when not to use it.

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, and it does well: 'Read' signals a safe read operation, 'owner-only' discloses access scope, and 'without exposing them to buyers' describes a privacy behavior. It does not specify permissions, errors, or output formatting, but those are less critical for a one-parameter read 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?

A single sentence that front-loads the core purpose ('Read actionable owner-only failures and next actions') and appends the privacy qualifier. There is zero 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 one-parameter, read-only tool with no output schema, the description covers what is returned (failures and next actions), for whom (owner-only), and a key privacy constraint (not exposed to buyers). It could add an explicit cross-reference to get_ai_pack_report, but nothing essential is missing for selecting and invoking it.

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

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

Input schema coverage is 100% and the only parameter productId already has schema documentation ('Tetrees AI Pack product id'). The description adds no additional parameter semantics, so the schema-carries-the-load baseline of 3 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 states a specific action ('Read'), a concrete resource (private Agent AVCP feedback), and a scope ('owner-only failures and next actions'). The phrase 'without exposing them to buyers' marks it as the private counterpart to buyer-facing report tools, so an agent can tell it apart from get_ai_pack_report.

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 'owner-only' wording and the 'without exposing them to buyers' qualifier give clear context that this is for the pack owner's private review rather than buyer-facing reporting. It does not explicitly name alternatives or state when not to use it, so it stops short of 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?

With no annotations, the description carries the behavioral burden, and it does add useful traits: the operation is a read ('Read'), the report is buyer-safe, version-bound, and only for published packs. However, it does not disclose authentication requirements, failure behavior for unpublished or nonexistent packs, or how the gates/scores are presented, so some behavioral context is still missing.

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 delivers the object, qualifiers, and scope with no repetition of the tool name or filler. Every phrase adds signal.

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

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

For a single-parameter read tool with no output schema, the description names what is returned (AVCP gates and scores) and the relevant constraints (buyer-safe, version-bound, published). It does not cover auth or error cases in detail, but those are minor for a low-complexity read 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 schema fully documents productId as a UUID, and the description adds a meaningful precondition beyond the schema: the productId must identify a published pack. This helps an agent avoid passing draft or unpublished pack IDs even though the parameter description itself does not say so.

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 read verb with a well-defined resource: the buyer-safe, version-bound Agent AVCP report for a published pack. The qualifiers 'buyer-safe' and 'published' clearly separate it from the sibling get_ai_pack_private_report without needing to mention it. An agent can identify what this tool returns and for which pack state.

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

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

It gives clear selection context: use this when you need the AVCP report that is buyer-safe and version-bound for a pack that is already published. It does not explicitly name alternatives or state when not to use it, but the published/buyer-safe framing makes the intended use obvious relative to the private report sibling.

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. 'Return' and 'before a run' imply a non-mutating inspection operation, and the description names the specific data categories returned. However, it does not explicitly state that no run is triggered or that no modifications occur, nor does it mention permission or error 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 sentence with no filler, front-loads the action word 'Return', and packs the tool's scope into a compact list. Every part of the sentence adds 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 low-complexity tool with only one well-documented parameter and no output schema, the description covers the essential return categories and the pre-run usage context. It does not describe the output structure, but the listed return areas are enough for an agent to determine whether this tool fits its needs.

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% for the single required parameter productId, and the schema describes it as a UUID product id. The tool description adds no further parameter-level detail beyond what the schema already provides, so baseline 3 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 uses a specific verb ('Return') and identifies a precise resource: Pack-declared hosted skills, ephemeral attachment policy, and user-controlled MCP/API extensions. This content profile is distinct from sibling tools like get_ai_pack_report or run_ai_pack, so an agent can tell what this tool uniquely provides.

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 'before a run' gives a clear temporal context for when to use this tool, distinguishing it from run-executing or post-run reporting tools. It does not explicitly name alternatives or exclusions, but the pre-run context is sufficient guidance for selection.

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 must carry the behavioral disclosure burden. The verb 'get' and 'Return' strongly imply a read-only operation, and 'exact next actions' clarifies it produces guidance rather than performing changes. However, it does not explicitly state that no state is modified or mention any authentication or access requirements.

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

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

A single sentence with no filler. The key verb, target categories, and outcome are all front-loaded. Every word 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?

The tool is simple with one parameter and no output schema, and the description gives a reasonable summary of what is returned. However, it omits the structure or format of 'exact next actions' and leaves the TAIP acronym unexplained, which could leave an agent uncertain about how to interpret or act on the result.

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

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

Schema description coverage is 100%, so the single productId parameter is already fully documented in the schema. The description does not add parameter-level detail, but the baseline of 3 applies when the schema does the heavy lifting.

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

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

The description states a specific verb ('Return') and resource ('requirements for AI Pack submission readiness'), and lists the distinct categories checked (TAIP, metadata, imagery) plus the added value ('exact next actions'). It is clear enough to distinguish from sibling submission/upload tools, though it does not explicitly contrast with 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 title and description imply this is a readiness check to run before submitting an AI pack, but no explicit when-to-use or when-not-to-use guidance is given. An agent must infer from the tool name and sibling context that it should be used ahead of submit_ai_pack_for_audition.

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 disclosure burden. It does convey a read-only 'List' action and 'current server pricing' implies live data. However, it does not mention response format, authentication, pagination, or whether the call has side effects or costs.

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 sentence with no filler, front-loaded with the verb and object. It communicates the full scope of the listing efficiently.

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 zero-parameter list tool with no output schema, the description covers the expected content: models, pricing, capabilities, BYOK support, and sample quotes. It could specify the return format more explicitly, but the described scope is largely complete.

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

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

The tool has zero parameters and 100% schema description coverage, so parameter documentation is unnecessary. The description still adds semantic value by listing the categories of data returned, such as BYOK support and sample Point quotes.

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 verb ('List') and resource ('agent models'), then enumerates concrete content categories: providers, pricing, capabilities, BYOK support, and Point quotes. It is clearly distinct from the AI-pack-focused sibling 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?

The description implies use when an agent needs the agent-model catalog, but it gives no explicit when-to-use or when-not-to-use guidance and names no alternatives. The siblings are all AI-pack operations, so inference is easy, but the description itself does not provide routing guidance.

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

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

With no annotations provided, the description carries the behavioral burden, and it does contribute meaningfully by stating that private memory contents are not exposed. This is a non-obvious privacy behavior that an agent cannot infer from the schema. As a read-style listing operation, it also implies no destructive side effects, though explicit permission or rate-limit details are absent.

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, tightly packed sentence that front-loads the action and resource, then lists the returned data categories and the privacy boundary. Every clause adds useful information with 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 one-parameter listing tool with no output schema, the description adequately communicates the return data categories: growth checkpoints, active checkpoint, and hosted slot/byte usage and limits. It also clarifies an important boundary by excluding private memory contents. It does not describe ordering, pagination, or prerequisites, but these are less critical for such a straightforward read 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 input schema covers the only parameter, productId, with 100% description coverage, so the schema already provides the necessary semantic meaning. The tool description adds no further detail about the parameter beyond what the schema states, so the 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 uses the specific verb 'List' and identifies a distinct resource: growth checkpoints, the active checkpoint, and hosted slot/byte usage and limits. This clearly differentiates it from sibling tools like get_ai_pack_runtime_profile or list_owned_ai_packs by naming the exact data domain. The added qualifier about not exposing private memory contents further sharpens the tool's purpose.

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 the tool is appropriate—when an agent needs growth history, active checkpoint, or slot/byte usage details—but it does not explicitly state when to prefer this over alternatives or when not to use it. The privacy caveat hints at a selection criterion, but no sibling alternatives or exclusions are named.

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 of behavioral disclosure. It indicates a read-style list operation and scopes the result set, but it does not mention ordering, pagination, completeness guarantees, or what happens if no packs are owned. This is adequate but not detailed.

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, well-structured sentence with no filler. The core action and scope are front-loaded, and every word contributes to understanding 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 zero-parameter list tool, the description is largely complete: it identifies the resource and the ownership categories. There is no output schema, so a small note about the expected return shape (e.g., a list of pack IDs/metadata) would improve completeness, but it is not strictly necessary for a simple list 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 no parameters, so the baseline is 4. The description still adds useful semantic context by defining exactly what kind of packs are included, which helps the agent understand the implicit result filtering even without an input 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 identifies the action ('List') and the resource ('owned Tetrees AI Packs'), and specifies three meaningful ownership categories: purchased, claimed-free, and seller-owned. This distinguishes it from sibling search/list tools like search_ai_packs and list_ai_pack_growth.

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 conveys the intended use case: retrieving packs available to the current account. It does not explicitly mention when not to use this tool or name alternatives, but the ownership scoping and zero-parameter interface make the context clear enough for simple selection decisions.

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

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

With no annotations, the description carries the full disclosure burden, and it does meaningful work: it states the tool only returns a plan and that 'Tetrees does not execute client extensions on its hosted servers,' which negates the primary behavioral risk for a prepare-style tool. It does not, however, cover side effects, whether approvedWriteMethods approvals persist, or explain the AVCP audition jargon.

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

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

Two sentences, both load-bearing: the first states the deliverable and scope, the second states the non-execution contract. No redundant restatement of the title and no filler, with the most distinguishing fact placed at the end as a deliberate boundary.

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 definition is adequate for a moderate-complexity prepare tool: it establishes the plan-not-execute contract and the input constraint. But with no output schema and no annotations, an agent still lacks the plan's format, whether invocation has side effects or creates persisted state, and how this connects to run_ai_pack or execute_client_extension for the actual execution step.

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%, so the description must partially compensate. It adds useful semantic context by enumerating what selected extensions reference — 'local skills, MCP tools, resources, prompts, or HTTPS APIs.' It does not illuminate the required task parameter or clarify the relationship between approvedWriteMethods and selectedExtensionIds beyond the schema's own one-line notes.

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 deliverable — 'a host-readable execution plan' — and a concrete scope: 'connecting this Pack to explicitly selected... local skills, MCP tools, resources, prompts, or HTTPS APIs.' The final sentence clarifies this is a plan-only tool, which differentiates it from execution-oriented siblings like run_ai_pack and execute_client_extension.

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 qualifier 'explicitly selected, Agent-AVCP-auditioned' communicates that the tool is for already-vetted extensions rather than discovery, which gives some usage context. However, no alternative tool is named and no explicit when-not-to-use guidance is provided; the boundary separating this from run_ai_pack or execute_client_extension must be inferred from the italic distinction that Tetrees does not execute client extensions on hosted servers.

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 side effects. It explicitly states 'does not mutate the pack' and 'creates a reviewable delta,' which gives an agent important safety and outcome context. It stops short of explaining whether other state changes occur or whether the operation is reversible, but it addresses the most critical behavioral concerns.

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 communicate the action, prerequisite, side-effect profile, and non-mutation guarantee. Every word earns its place, and the key operational constraint 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, the description covers purpose, prerequisite, and mutation safety. But there is no output schema and no explanation of what the 'reviewable delta' looks like or what the return value will be. It also does not position itself within the broader growth workflow, leaving the agent to infer follow-up steps.

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 has 0% description coverage, so the tool description must compensate. It does map runId to 'a successful run' and proposalIndex to 'a proposal,' which adds useful context. However, it does not explain how proposalIndex is derived, what the 'delta' represents, or how the proposal relates to run output, leaving some ambiguity for 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 states a clear action ('Select a proposal from a successful run') and identifies the resource ('AI Pack growth'). It also hints at the tool's distinguishing behavior by saying it 'creates a reviewable delta and does not mutate the pack.' It does not fully differentiate from the sibling select_ai_pack_growth_version, but the core purpose is specific and understandable.

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 'from a successful run' implies a prerequisite: the caller should have a completed run before using this tool. However, there is no explicit guidance about when to choose this over accept_ai_pack_growth, list_ai_pack_growth, or select_ai_pack_growth_version, and no 'when not to use' guidance is 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 disclosure burden. It usefully reveals that it publishes the exact current version, that AVCP gates must pass first, and that paid packs need Stripe Connect. However, it omits side effects, permissions, reversibility/publish-finality, and any response/error 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 compact, front-loaded sentences with no filler. The main condition appears first and the paid-pack exception is added only where relevant.

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 flat parameters, no output schema), so the gating and Stripe notes cover much of the needed context. But the confirmation parameter is left unexplained and there is no statement of what a successful publish returns or changes, leaving the definition adequate but 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?

Schema coverage is only 50%: productId gets a minimal repeated description and confirmation is documented only by its const value. The description adds no parameter-level meaning such as why the confirmation constant is required or how productId identifies the pack.

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?

States a specific action ('Publish the exact current version') on a defined resource (an Agent AVCP-passing pack), with the gate condition included. This clearly separates it from sibling tools such as upload_ai_pack, submit_ai_pack_for_audition, and update_ai_pack_draft.

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?

Gives an explicit precondition: invoke only after every mandatory Agent AVCP gate passes, and for paid packs ensure Stripe Connect is set up before checkout. It does not name alternatives or list when-not-to-use cases, but the context is clear enough for selection.

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 discloses that the tool returns a worst-case point reservation and that BYOK quotes zero points, but it does not state whether the tool has side effects, whether the quote is binding, or what happens on failure. This is partial disclosure, not full 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?

Two tightly written sentences, front-loaded with the core purpose and enriched with the funding-mode caveat. Every word earns its place and no redundant filler is present.

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

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

Given no annotations, no output schema, and 7 parameters with low schema coverage, a two-sentence description is too thin. It omits output shape, error behavior, parameter guidance, and relationship to sibling tools like run_ai_pack or quote_ai_pack_audition.

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 only 29%, so the description must compensate. It adds meaning for fundingMode by distinguishing points vs BYOK, but it says nothing about model, productId, enabledSkills, growthVersion, or token limits. With 7 parameters and most undocumented, this is insufficient.

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 ('Get') and resource ('exact worst-case Point reservation') tied to an AI Pack run, and adds the funding-mode distinction. This clearly differentiates quoting from actually running a pack, even without naming sibling 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?

The description gives clear context: use it before platform-funded inference, and BYOK mode results in zero model-point quotes. It implies the appropriate moment for invocation but does not explicitly name alternatives or state when not to use the tool.

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 must carry the full burden of behavioral disclosure. 'Return' suggests a read-like operation, but the description does not explain prerequisites, side effects, whether an audition must already exist, or how version binding behaves. This is a significant gap for a tool with no annotation safety hints.

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 with no filler words, and the main return object is front-loaded. It loses a point because the undefined acronym 'AVCP' and the jargon 'Point quotes' make the sentence dense and cryptic, though still compact.

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, no annotations, and no usage guidance, the description does not fully equip an agent to invoke the tool correctly. It identifies what is returned but not when to use it, what 'AVCP deliverables' mean, whether an audition must be run first, or what the returned data structure looks like. The presence of many related siblings increases the need for more context.

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

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

The input schema already describes the only parameter, productId, with 100% coverage. The description does not add additional meaning about parameter usage beyond tying the action to version-bound quotes. Per the baseline for high schema coverage, score 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 uses a clear verb ('Return') and a specific resource ('exact version-bound Point quotes and Agent AVCP deliverables'), so it states what the tool produces. It is not a tautology, and the 'version-bound' qualifier adds specificity. However, it does not explicitly distinguish itself from the similar sibling quote_agent_run.

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 alternatives such as quote_agent_run, run_ai_pack_audition, or get_ai_pack_report. The description implies the tool returns audition-related quotes, but it never states the conditions that should lead an agent to select it.

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 add meaningful behavioral traits: the preview is stateless, local files are request-scoped, and ownership gates BYOK, saved growth, and download. However, it does not disclose output behavior, side effects, authorization requirements, or failure modes, which prevents a higher score.

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

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

The description is appropriately concise: two sentences with no filler. It front-loads the core purpose and then packs the most important behavioral and ownership constraints into the second sentence. A bit of jargon like 'stateless base preview' and 'BYOK' could be clearer, but overall structure is efficient.

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

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

For a tool with 9 parameters, no annotations, and no output schema, this description is incomplete. It does not explain what the tool returns, how outputs are delivered, what happens on failed runs, or how this relates to sibling run/prepare/audition tools. The ownership and local-file details are valuable but do not fully equip an agent to call the tool correctly in varied contexts.

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 44%, so the description must compensate. It adds meaningful semantics for fundingMode ('Points fund...' vs 'Ownership unlocks BYOK'), attachmentPaths ('local files remain request-scoped'), and growthVersion ('saved growth'). However, it leaves several parameters—model, prompt, maxInputTokens, maxOutputTokens, productId—without additional explanation beyond the bare schema, so compensation is partial.

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 action: 'Run a Tetrees AI Pack' identifies a specific verb and resource. However, it does not explicitly differentiate run_ai_pack from close siblings like run_ai_pack_audition or prepare_local_ai_pack_run, leaving some ambiguity about which run-related tool to select.

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: 'Before acquisition, Points fund a stateless base preview' and 'Ownership unlocks BYOK, saved growth and download' signal when to use points vs BYOK. But it does not give explicit guidance on when to prefer this tool over alternatives such as run_ai_pack_audition or prepare_local_ai_pack_run, so the guidance remains implied rather than explicit.

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 does reveal that multiple gate types are executed and that confirmation is required, but it does not state whether running the audition mutates pack state, incurs cost, has long runtime, or how gate failures are reported.

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 dense, verb-first sentence with no filler, and the precondition is embedded reasonably early. The heavy jargon and long unbroken list of gate types reduce scannability, but the size is still appropriate for the content.

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 five parameters, no output schema, and no annotations, the description is thin: it omits return values, failure behavior, side effects, and how to set expectedPoints or expectedVersion. It provides only the confirmation precondition and gate scope, so an agent cannot confidently invoke it correctly.

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 20%, so the description must compensate but largely does not. 'Version-bound' loosely maps to expectedVersion and 'confirmation' aligns with the confirmation parameter, but expectedPoints, tier, and the confirmation constant are not semantically explained.

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

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

The description specifies a clear action ('Run'), a resource ('AI pack audition'), and a distinct set of gate categories, which separates it from generic run tools. However, 'Point confirmation' and 'AVCP' are unexplained, and the description does not say what the tool returns, so clarity is good but not complete.

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 'after explicit Point confirmation' implies a precondition, and 'version-bound' hints at when the tool applies. But it gives no explicit guidance on when to use this tool versus siblings like quote_ai_pack_audition, submit_ai_pack_for_audition, or run_ai_pack, and lists no 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 behavioral burden. 'Search' implies a read-only operation, and 'published exchange' clarifies scope, but the description does not explicitly confirm non-mutation, authentication expectations, or what kind of results are returned.

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 adds meaning, and it is appropriately sized for a simple search tool.

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 low-complexity search tool with two optional parameters, the core behavior is described. However, with no output schema and no annotations, the description omits details about return shape, result count semantics, and whether the search is fuzzy, exact, or ranked. This leaves some inference to the agent.

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 adds meaning for the query parameter by stating it should contain a task or capability, but it does not describe the limit parameter or how results are ordered/filtered. The limit is at least self-explanatory from its name and schema constraints.

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 ('Search'), identifies a distinct resource ('published agent-asset exchange'), and states the search dimension ('by task or capability'). This clearly differentiates it from siblings like list_owned_ai_packs and get_ai_pack_runtime_profile.

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 alternatives such as list_owned_ai_packs or get_ai_pack_submission_readiness. The description implies a browsing/search use case but does not state exclusions or direct the agent to a more appropriate sibling.

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 behavioral disclosure burden. It adds meaningful safety context ('selection never rewrites the seller Pack') and explains the effect of sequence 0, which is valuable. It does not mention permissions, reversibility, or effects on existing runs, but the non-rewriting guarantee is a significant disclosed trait.

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 the primary action front-loaded. The second sentence adds only high-value information: the sequence-0 special case and the guarantee that the seller Pack is never rewritten. 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?

For a mutation tool with no annotations and no output schema, the description leaves important gaps. The required confirmation parameter is not explained as a deliberate consent token, which is critical for safe autonomous invocation. Prerequisites such as Pack ownership are only implied, and the description does not say what happens after selection.

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%, and the description compensates partly by explaining the sequence parameter ('Use sequence 0 to return to the signed base Pack'). The productId is only minimally described in the schema, and the required confirmation parameter is given no semantic meaning anywhere.

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 first sentence clearly identifies the operation ('set this account's default hosted intelligence checkpoint') and the target resource ('an owned Pack'), distinguishing selection from creation, listing, or execution. It does not explicitly name a sibling alternative, but the verb and resource are specific enough to avoid confusion.

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 useful context, such as using sequence 0 to return to the signed base Pack, and implies this tool is for setting an account-level default. However, it never explicitly states when to use this tool over siblings like propose_ai_pack_growth, accept_ai_pack_growth, or run_ai_pack, nor does it provide 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 behavioral disclosure burden. It adds meaningful context by describing the queue as 'immutable' and requiring 'readiness' to pass, implying a consequential and irreversible state transition. It does not mention the confirmation constant or failure behavior, but the core side effect is disclosed.

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 communicates the action and key preconditions efficiently, though domain jargon like 'immutable Agent AVCP queue' reduces immediate clarity.

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 no annotations, no output schema, and simple parameters, the description covers the readiness precondition and the immutable destination. It omits what happens if readiness has not passed, whether the submission can be withdrawn, and the role of the confirmation token, leaving some workflow details to inference.

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 50%: productId has a description, while confirmation only has a const value. The description adds context that productId refers to a complete owner-bound draft, but it does not explain the confirmation parameter. The const value 'SUBMIT_AGENT_PACK' is self-documenting, so invocation is still feasible.

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 ('Move') and names a clear resource ('complete owner-bound draft' into the 'Agent AVCP queue'), so the core action is understandable. It distinguishes itself from run/publish siblings by focusing on queue submission, though it does not explicitly name a sibling and 'Agent AVCP' jargon is unexplained.

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 'after readiness passes' gives an implied precondition and suggests a workflow with get_ai_pack_submission_readiness. However, it does not explicitly state when to use this tool versus quote_ai_pack_audition, run_ai_pack_audition, or publish_ai_pack, and no exclusions 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 full burden. It does disclose an important behavioral trait: pack versions are never overwritten, only metadata is updated. But it does not clarify whether the update is partial or full replacement, what permission is required, or what the response looks like.

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

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

The description is two short sentences with no wasted words. The action and scope are front-loaded, and the immutability warning is placed second while still being 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 high-complexity tool with 8 parameters, a nested listing object, no output schema, and no annotations. The description is too brief to be complete; it leaves out update semantics, validation behavior, and field-level guidance. The schema provides structural constraints but not the operational context an agent needs.

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%, with only productId documented. The description says 'public metadata' but does not map that to the actual parameters such as tags, models, listing, priceUsd, capabilities, businessUseCase, or shortDescription. Given the low coverage, the description should compensate but does not.

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

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

The description states a specific verb ('Update') and a clear resource ('seller-owned public metadata'), and explicitly contrasts this with immutable pack versions. This distinguishes the tool from version-related operations like upload_ai_pack or publish_ai_pack. The title reinforces that it targets listing details.

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 'seller-owned public metadata' implies the intended scope, and 'immutable pack versions are never overwritten' gives an exclusion for version content. However, no sibling tool is named, and there is no explicit direction about when to choose this tool over create_ai_pack_draft or publish_ai_pack.

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?

Since no annotations are provided, the description carries the burden of behavioral disclosure. It does reveal that the operation reads a local path, uses a private upload contract, and commits an immutable higher version. However, it omits important consequences such as required permissions, behavior on version collisions, whether the commit is reversible, and whether this also publishes or only uploads.

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 dense sentence with no filler: every clause contributes the source path, the transfer contract, or the version-commit outcome. The heavy domain jargon such as 'TAIP/1 envelope' and 'product-scoped private upload contract' may reduce accessibility slightly, but the structure itself is efficient and front-loaded.

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

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

With no output schema and no annotations, the description covers the main action but leaves out surrounding context an agent needs, such as return value, ownership/permission requirements, version-collision errors, and how this step relates to publish/submit/draft siblings. It is adequate for a basic invocation but not fully complete for safe autonomous use.

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 25%, so the description must add meaning beyond the schema. It maps packPath to a local .taip path, productId to product scope, and version to a higher immutable version; changelog is not addressed, and the meaning of 'higher' is not fully specified. This is partial compensation, not complete.

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

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

The description names a specific operation—read a local .taip file, transfer it through a product-scoped upload contract, and commit a version—while the title adds 'TAIP/1 envelope'. This clearly distinguishes the tool from siblings like upload_ai_pack_image or create_ai_pack_draft because it is explicitly about uploading and committing a pack envelope as an immutable higher version.

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 clear context: this tool is for an explicit local .taip path and a version commit, which lets an agent infer it belongs in release/versioning flows rather than draft editing or image upload. It does not explicitly name alternatives or state when not to use it, so it stops short of top marks.

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 must carry the full behavioral burden. It discloses accepted file formats and that the image is local, but it does not mention whether an existing preview image is overwritten, whether the pack must be in a draft state, what succeeds/fails look like, or any size/validation limits.

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 sentence with no filler, front-loaded with the action and resource. Every phrase adds useful information: 'one', 'explicit local', the format list, and 'seller-owned pack listing'.

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 is adequate for a simple 2-parameter upload tool, and the parameter schema plus format list cover the core invocation details. However, with no output schema and no annotations, it omits return value expectations, overwrite behavior, and ownership validation steps, leaving minor but real gaps for an agent deciding whether the call succeeded.

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 productId with a description, but imagePath is only typed as a string. The description compensates by specifying accepted formats (JPEG, PNG, WebP, AVIF) and 'local' path semantics, adding meaningful detail to imagePath. It does not explicitly map the formats to imagePath, but the inference is straightforward.

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 ('Upload') and resource ('preview image to seller-owned pack listing'), and scopes the operation to image formats only, clearly distinguishing it from the sibling 'upload_ai_pack'. The phrase 'seller-owned' also sets expectations about authorization 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?

The description implies the tool is for uploading a preview image to a pack listing, and the 'seller-owned' qualifier gives some context about when it applies. However, it does not explicitly name alternatives such as 'upload_ai_pack' or state when not to use this tool, leaving some routing work to the agent.

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