Tetrees AI

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

Annotations already indicate the tool is not read-only, so the mutation implied by 'append' is consistent. The description adds context about normalization options and a deterministic growth gate, but does not disclose what happens on gate failure or side effects beyond appending to the owner.

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, dense sentence that front-loads the purpose and then lists the steps. It is concise and avoids redundancy, though the jargon may reduce clarity for an uninitiated agent.

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 omits critical domain context: what a 'delta' is, what the 'growth gate' entails, the meaning of the confirmation constant, and the role of sequence. With no output schema and minimal annotations, an agent lacks enough information to call this correctly without external knowledge.

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% (only productId has a description). The description hints at the normalization parameter ('Tetrees Agent or provider-free portable normalization') but does not explain sequence, confirmation, or normalization defaults. It does not compensate sufficiently for the low schema coverage.

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

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

The description clearly identifies the action (accept growth) and the resource (AI Pack) with a specific process: normalize, run gate, append. It distinguishes from sibling tools like propose_ai_pack_growth and select_ai_pack_growth_version by implying it is the final acceptance step, though it does not explicitly name these alternatives.

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

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

No explicit guidance on when to use this tool versus propose_ai_pack_growth or select_ai_pack_growth_version. The phrase 'append it to this owner only' hints at a finalization step, but there is no clear workflow context or exclusions 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?

Annotations already indicate this is a non-read-only but non-destructive operation. The description adds the prerequisite/gating context for pack operations, but it does not disclose side effects, irreversibility, or what 'recording' the agreement changes on the account.

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 efficient sentence that front-loads the core action and follows with the required-when context. 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 simple one-parameter tool with annotations covering the read/write/destructive profile, the description covers the critical usage gate. It is slightly incomplete regarding the exact confirmation value, but the schema provides that detail.

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 should compensate by explaining the confirmation parameter. It does not mention 'confirmation' or the required constant 'ACCEPT_TETREES_AI_PACK_TERMS', leaving the agent to infer the parameter's meaning solely from the schema and tool name.

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 ('Record') and identifies the resource ('current account agreement') plus the business context ('required before creating, uploading, auditioning, or publishing a pack'). This clearly differentiates it from sibling pack-management 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 explicitly states when the tool is needed: before creating, uploading, auditioning, or publishing a pack. It does not name alternatives, but no direct alternative exists among the siblings, so the context is clear.

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

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

Annotations already declare readOnlyHint=false (mutation) and destructiveHint=false. The description adds that the entitlement is 'revocable' and that the pack must be 'published free', which are useful behavioral traits beyond the annotations. However, it does not disclose side effects, idempotency, or what happens on duplicate entitlements, so the description provides moderate additional context.

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

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

The description is a single, front-loaded sentence with zero waste. It states the core action and the key constraint immediately, making it highly efficient for an agent to parse.

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

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

Given the tool's simplicity (one parameter, no output schema, minimal annotations), the description covers the essential purpose, condition, and parameter constraint. It could mention whether the operation is idempotent or what happens if the entitlement already exists, but these are minor gaps for a tool of this complexity.

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

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

The schema covers productId at 100% with a description of 'Tetrees AI Pack product id'. The description adds semantic constraints: the pack must be published and free, and that Stripe is not involved. This goes beyond the schema, refining what constitutes a valid productId for this tool.

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 ('Create'), resource ('a revocable entitlement for a published free pack'), and a condition ('without Stripe'). It clearly distinguishes the tool's purpose from siblings like accept_ai_pack_terms or create_ai_pack_draft, making it unambiguous what this tool does.

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

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

The description gives clear context on when to use this tool: for published free packs, specifically bypassing Stripe. However, it does not explicitly name an alternative tool for paid acquisitions or explain when not to use it, leaving a slight gap in routing. The condition 'without Stripe' implies a distinction but no explicit sibling reference.

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

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

Discloses creation, owner-binding, and payment/terms conditions beyond the readOnly/destructive hints. Does not mention failure modes, validation, or success output, but enough for a create-draft operation.

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

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

Two concise sentences with the main action first and conditions second; 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 complex nested schema with no output schema, the description is too sparse. It omits parameter-level semantics, success/error behavior, and how the terms-acceptance prerequisite is enforced.

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

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

Schema has zero description coverage and nested listing fields, while the description only hints at 'buyer guide' and 'example uses'. It does not explain priceUsd, capabilities, models, or the nested listing properties.

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

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

Clearly states it creates a draft AI Pack listing, with owner binding and required buyer guide/example uses. Distinguishes from sibling tools like update_ai_pack_draft and publish_ai_pack by using 'create' and '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 prerequisite guidance ('Accept current terms first') and a condition ('Free drafts do not require Stripe Connect'), but does not explicitly name alternatives such as update_ai_pack_draft or 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?

Annotations already flag non-read-only, non-destructive behavior, and the description adds useful context: the URL is short-lived, entitlement-gated, and the artifact is a signed .taip envelope. It does not discuss possible side effects or failure modes, but the added gating/lifetime detail goes beyond the structured fields.

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

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

Single sentence with all essential facts front-loaded: the action, the returned object, its lifetime, access restriction, and artifact type. No filler or redundancy beyond the title's 'owned' being reflected as 'entitlement-gated'.

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 with no output schema, the description adequately conveys the return value (short-lived URL) and the prerequisite (entitlement). It could mention error/denial behavior for unowned packs, but complexity is low enough that the description is nearly complete.

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

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

Schema coverage is 100%, so productId is already described as a UUID with a clear name. The description adds mild context by tying the parameter to an owned/entitled pack, but no syntax or extra meaning beyond the schema is necessary.

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 verb ('Return') and resource ('short-lived entitlement-gated URL for the signed .taip envelope'), clearly identifying it as a download/obtain-URL operation rather than upload/publish/run. The title's 'owned' qualifier plus entitlement-gated phrasing distinguishes it from acquisition or search 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?

The phrase 'entitlement-gated' implies use only for packs the caller owns, but the description never explicitly states when to use this over siblings like get_ai_pack_runtime_profile or run_ai_pack, nor mentions any exclusions. Usage is implied, not instructed.

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

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

The description adds meaningful behavioral context beyond the annotations: credentials/config stay in the local MCP process (a security disclosure), and write methods require an exact one-call confirmation (a mutation guardrail). These align with openWorldHint=true and readOnlyHint=false, and there is no contradiction with destructiveHint=false.

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

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

Three dense sentences with no filler: the core action is front-loaded, followed by a security note and a safety requirement. Every sentence earns its place, and the structure makes the most critical behavioral constraints immediately visible.

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 powerful open-world tool with 5 parameters, nested objects, and no output schema, the description covers the safety contract (local credentials, write confirmation) and channels, but it omits what the return value looks like, how method discovery works, and error/timeout behavior. The complexity is high enough that these gaps leave the agent partly under-informed.

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% (productId and writeConfirmation have descriptions), so the description must compensate for methodId, extensionId, and arguments. It clarifies that exactly one method is invoked and reinforces the exact write-confirmation format, but it adds no meaning for the arguments object or the format/length constraints of methodId and extensionId.

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 (execute), a precise resource (one method from an owned Pack), and the execution channels (HTTPS API, nested MCP server, local-skill child process). Combined with the title's 'auditioned client-owned extension,' it clearly differentiates this from siblings like run_ai_pack (whole-pack execution) and run_ai_pack_audition (pre-ownership evaluation).

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: the extension must be owned and auditioned (from the title), and the method-level granularity distinguishes it from whole-pack tools. However, it never explicitly names alternatives or states when NOT to use this tool versus run_ai_pack or prepare_local_ai_pack_run, leaving the agent to infer the routing.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds meaningful behavioral context: the report is owner-only and specifically avoids exposing content to buyers, which is a privacy/visibility trait beyond the annotations. It does not mention auth requirements or rate limits, but for a read-only tool with these annotations, this is 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 description is a single 12-word sentence with no filler. It front-loads the core action ('Read actionable owner-only failures and next actions') and then appends the privacy qualifier. 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?

For a tool with one well-documented parameter, no output schema, and read-only annotations, the description is complete. It tells the agent what the report contains, who it is for, and the privacy boundary. No additional context is needed to correctly select or invoke this tool.

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

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

The input schema covers the single parameter productId with a clear description ('Tetrees AI Pack product id'), achieving 100% schema description coverage. The description does not add any parameter-specific meaning, but none is needed since the schema already fully documents it, yielding the baseline score of 3.

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 'Read' with a clear resource: 'actionable owner-only failures and next actions'. The phrase 'without exposing them to buyers' adds the private/owner-only scope, distinguishing it from the sibling 'get_ai_pack_report'. The title reinforces the purpose with 'private Agent AVCP feedback'.

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 clearly implies the tool is for owners to read their own private report, contrasted with exposing information to buyers. This gives clear contextual guidance for when to use it. However, it does not explicitly name alternative sibling tools or state a direct 'use this instead of X' condition, so it falls just short of full explicitness.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description's 'Read' verb is consistent. It adds useful context about the report being buyer-safe and version-bound, but it does not describe return format, pagination, or any access considerations. With annotations covering the safety profile, this is adequate but not rich.

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, focused sentence with no filler. Key scoping qualifiers are front-loaded, and every phrase adds 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 simple read-only tool with one well-documented parameter and safety annotations, the description is mostly complete. It identifies what is returned ('gates and scores') even without an output schema, though a bit more detail about the report structure would make it 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 only parameter, productId, is described in the schema with 100% coverage. The description does not add parameter-level detail, but with full schema coverage the baseline of 3 is appropriate.

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

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

The description clearly states a specific verb ('Read') and resource ('Agent AVCP gates and scores') with an explicit scope ('for a published pack'). The qualifiers 'buyer-safe' and 'version-bound' help distinguish this from sibling tools like get_ai_pack_private_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 description conveys a clear usage context: use this for buyer-safe, published pack reports. It does not explicitly name alternatives or state when not to use it, but the published/private contrast is strongly implied by the sibling 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?

Annotations already cover readOnlyHint, openWorldHint, and destructiveHint, so the safety profile is clear. The description adds meaningful behavioral context by enumerating the specific categories of data returned, which goes beyond the annotations without contradicting them.

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

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

The description is a single, compact sentence that front-loads the action and then clearly lists the returned components. There is no filler or repetition of information already present in the schema or annotations.

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

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

With no output schema, the description usefully names three categories of returned information, which gives an agent a concrete sense of the tool's output. It could go slightly further by noting that no run is executed, but the 'before a run' phrasing already implies that.

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% and the productId parameter is already documented as 'Tetrees AI Pack product id.' The description adds no new parameter-level information beyond implying the productId identifies the Pack being inspected, so the schema carries the burden and the baseline of 3 is appropriate.

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

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

The description uses a specific verb ('Return') and names a precise resource: the Pack-declared hosted skills, ephemeral attachment policy, and user-controlled MCP/API extensions. It also positions the operation temporally ('before a run'), distinguishing it from report or audit 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?

The description clearly indicates the timing for use ('before a run'), giving an agent a clear contextual trigger. It does not explicitly name alternatives or exclusions, but the 'before a run' qualifier provides enough usage context.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description is consistent with them — no contradiction. The description adds mild behavioral context by indicating the tool is prescriptive (returns 'exact next actions'), not just a status listing, but adds nothing about auth needs, rate limits, or response format.

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 with zero filler. The verb, resource, and output value all appear immediately, and 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?

For a simple one-parameter, read-only check tool with safety covered by annotations, the description adequately conveys what will be returned. Although there is no output schema, the description states the return content (requirements and next actions). Slightly more explicit routing versus the many related siblings would make it fully complete.

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

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

Schema description coverage is 100%, and the single productId parameter is fully documented as a UUID with a clear description. The tool-level description adds no parameter-level meaning beyond what the schema already provides, so the 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 names a specific verb ('Return') and a clearly scoped resource (AI Pack submission readiness), plus the concrete output content (missing TAIP, metadata, imagery requirements and next actions). This distinguishes it from sibling tools like get_ai_pack_report or submit_ai_pack_for_audition, which serve different purposes.

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 tool name and description strongly imply it is a pre-submission/pre-publish gate check, but the description never explicitly says 'use before submit_ai_pack_for_audition or publish_ai_pack' or states when not to use it. Usage context is implied by the name 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description does not need to restate safety. It adds behavioral context by specifying exactly what data is returned (models, pricing, capabilities, BYOK support, quotes). However, it does not disclose output format, pagination, or rate limits, which are not covered by annotations but could be relevant for a listing tool.

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

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

The description is a single, efficient sentence that front-loads the main purpose and lists the specific data returned. There is zero waste or redundancy. It is appropriately sized for a simple list operation.

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

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

For a tool with no parameters, no output schema, and a clear read-only purpose, the description is complete. It covers what is listed and the specific aspects included. The low complexity means nothing essential is missing.

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, so the description carries no parameter burden. Per the rubric, a baseline of 4 is appropriate when there are no parameters to document. The description adds no parameter semantics, but none are needed.

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

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

The description clearly states the verb 'List' and the resource 'agent models' from specific providers (OpenAI, Claude, Z.AI), along with additional details (pricing, capabilities, BYOK support, Point quotes). This is distinct from sibling tools, which all relate to AI packs, not model listings. The purpose is unambiguous.

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

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

While there is no explicit 'use when' statement, the description makes it evident that this tool is for retrieving model information. Sibling tools are all about AI packs, so no confusion exists. The context is clear, and no exclusions are needed since there are no overlapping alternatives.

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

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

Beyond the readOnlyHint annotation, the description discloses that it does not expose private memory contents, which is valuable behavioral information. It also specifies the exact data returned (checkpoints, usage, limits) without detailing output format.

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

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

The description is a single, well-structured sentence that efficiently conveys all necessary information without redundancy or unnecessary detail.

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 listing operation with one parameter, the description is complete. It specifies what is returned and what is excluded, requiring no additional context for correct invocation.

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

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

The only parameter, productId, is described as 'Tetrees AI Pack product id', which is clear and sufficient. Schema coverage is 100% since the parameter is fully documented.

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

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

The description clearly states the tool lists growth checkpoints, the active checkpoint, and slot/byte usage and limits. It also explicitly mentions what it avoids (private memory contents), making the purpose unambiguous.

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

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

The description does not explicitly mention when to use this tool versus alternatives like search_ai_packs or list_owned_ai_packs. The 'without exposing private memory contents' phrase gives a partial hint, but no direct comparison or usage scenario 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?

Annotations already indicate read-only and non-destructive behavior. The description adds that it lists packs available to the account, which is consistent and slightly informative, but does not go beyond the annotations in terms of behavioral disclosure.

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

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

The description is a single, concise sentence that conveys all necessary information without redundancy. It is well-structured and immediately understandable.

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

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

Given the absence of an output schema and parameters, the description is sufficiently complete. It explains what the tool returns (a list of owned packs) and the scope, which is adequate for an agent to use 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?

The tool has no parameters, so the baseline score applies. The description correctly indicates that no input is needed, and the lack of parameters is appropriate for a listing operation.

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 (list) with a specific resource (owned AI packs) and defines the scope (purchased, claimed-free, seller-owned) available to the account. It effectively distinguishes this tool from siblings like search_ai_packs and list_agent_models.

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

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

The description implies a use case (listing account-owned packs) but does not explicitly state when to prefer this over alternatives like search_ai_packs. It provides some context about the scope but lacks direct guidance on tool 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?

Annotations already mark the operation as read-only and non-destructive. The description adds meaningful behavior beyond that: the tool does not execute client extensions on hosted servers and only returns a plan, preventing the agent from expecting side effects or actual execution.

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 sentences with no filler. The core action is front-loaded, and the second sentence adds a critical caveat that prevents a common misunderstanding about execution. 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?

For a planning-only tool with read-only annotations, the high-level output description ('host-readable execution plan') and non-execution caveat give a solid picture. There is no output schema, so slightly more detail about plan contents would improve completeness, but it is adequate for selecting and invoking this tool alongside its siblings.

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

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

Schema covers productId and approvedWriteMethods but leaves task and selectedExtensionIds undocumented (50% coverage). The description adds selection/audition context and the list of connection types, which helps interpret selectedExtensionIds, but it does not explain required task semantics or fully map concepts to specific parameters.

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

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

States a specific verb and artifact: it returns a host-readable execution plan. It also names what is being connected (selected local skills, MCP tools, resources, prompts, HTTPS APIs) and clearly positions this as a planning step rather than an execution step, differentiating it from run/execute 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?

The description gives clear context: use this when connecting a Pack to explicitly selected, Agent-AVCP-auditioned local extensions. The final sentence implies this is not the tool for executing on hosted servers, but it does not explicitly name alternative siblings or provide a direct when-not-to-use statement.

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

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

Annotations already mark destructiveHint=false, and the description adds value by specifying that a reviewable delta is created while the pack is not mutated. This gives a concrete side-effect picture beyond the structured hints, with no contradiction.

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

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

Two short sentences with no waste: the action comes first and the side-effect caveat second. It avoids restating the title or schema.

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

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

Covers the core invocation context and non-mutating behavior for a simple 2-parameter tool. However, with no output schema and no explanation of how proposals are surfaced or what a reviewable delta entails, an agent still has to infer part of the workflow.

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 carry the parameter meaning. It only loosely maps 'successful run' to runId and 'a proposal' to proposalIndex, without explaining how proposalIndex relates to run output or how to identify a successful run. This is too thin to compensate for the absent schema descriptions.

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

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

States a specific action (select a proposal) and a concrete outcome (creates a reviewable delta, does not mutate the pack). The resource is clear enough to differentiate from accept_ai_pack_growth, though it does not explicitly name sibling alternatives like select_ai_pack_growth_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?

Implies when to use: after a successful run and when a non-mutating, reviewable delta is desired. It does not state exclusions or directly name the accept/select alternatives, so routing among the many sibling tools is left 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?

Annotations already indicate readOnlyHint=false and destructiveHint=false, so the publish action is understood to mutate state. The description adds useful behavioral context: it publishes the 'exact current version' only after gates pass, and paid packs have an additional Stripe Connect prerequisite. No contradiction with annotations.

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

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

Two sentences, no filler. The main action is front-loaded, followed by the gating condition and the paid-pack caveat. Every sentence adds necessary 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 publish operation with two simple parameters, the description covers the key workflow conditions: AVCP gate passage and Stripe Connect for paid packs. It does not explain the AVCP gate mechanism or explicitly mention the confirmation parameter, but those are either domain concepts or covered by the schema.

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 describes productId as 'Tetrees AI Pack product id' and confirmation is a self-documenting const value ('PUBLISH_AGENT_PACK'). Schema coverage is only 50%, and the description does not add parameter-level detail, but the const value and the simple two-parameter schema carry the semantic weight adequately.

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 ('Publish the exact current version') and the resource (an Agent AVCP-passing pack), and adds the necessary precondition that all mandatory Agent AVCP gates must pass. This distinguishes it from sibling tools like upload_ai_pack or submit_ai_pack_for_audition, which handle earlier stages of the pack lifecycle.

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 when this tool should be used: after every mandatory Agent AVCP gate passes. It also adds a conditional requirement for paid packs ('Stripe Connect before checkout'). It does not explicitly name alternatives, but the conditions are clear enough to route an agent to this tool versus siblings.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description is not burdened with basic safety disclosure. It adds useful behavioral context by explaining the 'exact worst-case' nature of the quote and that BYOK mode returns zero points, but it does not detail other behaviors such as failure modes, quota constraints, or what happens with invalid inputs.

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 deliver the essential purpose and a key special case with no filler. The most important information is front-loaded, and 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?

With 7 parameters, no output schema, and low schema coverage, the description is not complete enough for an agent to confidently invoke the tool correctly. It explains the high-level quote purpose and BYOK behavior but offers no guidance on required parameters, return format, or constraints around token limits and skills.

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 29%, so the description should compensate for the many undocumented parameters. It does not explain model, productId, enabledSkills, maxInputTokens, maxOutputTokens, or growthVersion beyond the schema. The only parameter-related insight is the BYOK/funding-mode distinction, which is not named explicitly and is insufficient for a 7-parameter tool.

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 verb ('Get') and resource ('worst-case Point reservation') and ties it to platform-funded inference, which makes the tool's purpose immediately understandable. It does not explicitly distinguish itself from the sibling 'quote_ai_pack_audition', but the wording 'AI Pack run' provides reasonable 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 gives clear context for when to use the tool: before using platform-funded inference, to obtain a Point reservation. It also notes the BYOK condition where the quote is zero points. However, it does not explicitly state when not to use it or name alternatives, 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds that it returns version-bound quotes and AVCP deliverables, which is useful but not deep. No contradiction exists; the description does not reveal additional behavioral traits like permission requirements or response format.

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 that immediately states the action and output. Every word earns its place with no redundancy.

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

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

For a simple read-only tool with one parameter and no output schema, the description is reasonably complete. It specifies what is returned but does not detail the format or any nuances, though given the low complexity and annotation coverage, it is adequate.

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 schema fully documents the single productId parameter. The description adds no additional meaning about the parameter beyond what the schema states. Baseline 3 is appropriate.

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

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

The description uses a specific verb 'Return' and identifies the resource as 'exact version-bound Point quotes and Agent AVCP deliverables', which clearly states what the tool does. It is distinct enough from the sibling 'quote_agent_run' by the 'audition' and 'AVCP' context, though it doesn't explicitly call out the difference.

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 'quote_agent_run' or 'run_ai_pack_audition'. It simply states the function without any context or exclusions, leaving the agent to infer usage.

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

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

Annotations already indicate readOnly=false and destructive=false. The description adds useful context around cost model (Points vs BYOK), statelessness of preview runs, and request-scoped local files, which goes beyond the annotations.

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

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

The description is concise at two sentences with no filler. The phrasing is somewhat cryptic and could be clearer, but it remains 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?

With 9 parameters and no output schema, the description leaves significant gaps: it does not mention return values, errors, prerequisites, or how required parameters like prompt and model should be used. The licensing context helps but is not enough for full operational clarity.

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%, and the description partially compensates by clarifying fundingMode (Points/BYOK), growthVersion (saved growth), and attachmentPaths (request-scoped files). However, model, prompt, and token parameters still lack explicit semantics in either the schema or 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 clearly states 'Run a Tetrees AI Pack,' identifying the verb and resource, and is distinct from sibling tools like run_ai_pack_audition or quote_ai_pack_audition. It does not fully detail execution semantics, but the core purpose is apparent.

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 does not explicitly say when to use this tool versus alternatives such as run_ai_pack_audition or prepare_local_ai_pack_run. The description focuses on licensing states (Points, acquisition, ownership) rather than actionable usage guidance.

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

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

Annotations already indicate this is not read-only and not destructive, so the description is not responsible for the full safety profile. The description adds context that the run is version-bound and gated by explicit confirmation, which is useful behavioral framing. Still, it does not disclose what state changes occur if gates pass or fail, or whether the audition produces a report or modifies the pack.

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 minimal fluff and front-loads the action. The list of gate types is efficient, though jargon like 'Point confirmation' and 'AVCP' adds compactness at the cost of 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?

With no output schema and sparse parameter documentation, the description needed to explain return values, side effects, and prerequisites more thoroughly. It supplies only a confirmation precondition and a gate list, leaving the agent without enough context about what a successful or failed audition looks like or what state changes occur.

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%, and the description does not meaningfully explain the key parameters. Terms like 'version-bound' and 'Point confirmation' loosely allude to expectedVersion and confirmation/expectedPoints, but productId, tier, expectedPoints, and expectedVersion are not clearly described. The description fails to compensate for the schema's sparse 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 uses a specific verb ('Run') with a clear resource ('ai_pack_audition') and enumerates the exact gate categories: integrity, model, regression, memory-isolation, injection, growth, and cost. This makes the tool's core function identifiable, though it does not explicitly differentiate it from siblings like quote_ai_pack_audition 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?

It states a precondition ('after explicit Point confirmation'), which implies the tool should only be run after confirmation is provided. However, it gives no explicit guidance on when to choose this tool over alternatives such as quote_ai_pack_audition, submit_ai_pack_for_audition, or run_ai_pack, nor does it mention any exclusions.

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

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

Annotations already indicate read-only and non-destructive behavior. The description adds that it searches only published exchange items, but it does not disclose return format, result ordering, or empty-result 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, focused sentence with no unnecessary words 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 search tool with no output schema, the description provides enough context to understand its purpose and primary input, though return details are left unspecified.

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 names and constraints for query and limit, and the description clarifies that query is by task or capability, but it does not explicitly explain the limit parameter or how results are filtered.

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

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

Clearly states a specific action ('Search') and resource ('published agent-asset exchange'), with a distinct scope ('by task or capability') that separates it from sibling tools like list_owned_ai_packs.

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 for finding published packs but does not explicitly state when to prefer this tool over alternatives such as list_owned_ai_packs or get_ai_pack_report.

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?

Transparent about side effects: states that selection does not rewrite the seller Pack. It does not mention auth or rate limits, but the annotations have no safety hints. The description adds meaningful behavioral context beyond the raw annotations.

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

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

Two concise sentences with no redundancy. Every phrase adds value, and the most critical information (action, scope, special case, side effect) 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 simple tool with three parameters and no output schema, the description covers the purpose, a special value, and a side-effect caveat. It does not explain the confirmation constant or productId, but these may be inferred from naming or context.

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

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

Schema description coverage is only 33% (one of three parameters described). The description explains the 'sequence' parameter via the 'sequence 0' instruction, but productId and confirmation remain undocumented. The description does not fully compensate for the low 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?

Clearly states the action: 'Set this account's default hosted intelligence checkpoint for an owned Pack.' The verb 'Set' and the target resource are explicit, and the scope ('for an owned Pack') distinguishes it from related tools like accept or propose.

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

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

Provides a concrete usage instruction ('Use sequence 0 to return to the signed base Pack') and clarifies a key behavior ('selection never rewrites the seller Pack'). It does not explicitly compare with sibling tools, but the guidance is actionable.

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

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

Annotations only convey coarse safety hints (not read-only, not open-world, not destructive). The description adds meaningful behavioral context by stating the draft is moved and the target queue is immutable, implying the submission is a one-way transition. It does not describe result or error 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 compact sentence with no filler. Every clause contributes information: the action, the target object, the destination's immutability, and the readiness condition.

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 tool with no output schema, the description captures the key workflow conditions: the object must be a complete owner-bound draft, readiness must have passed, and the queue is immutable. It does not spell out the confirmation parameter or how to obtain readiness, but those are available from schema constants and sibling tool names.

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 describes productId but leaves confirmation undocumented, giving 50% coverage. The description clarifies that productId should reference a complete owner-bound draft, which adds context beyond the schema, but it does not compensate for the confirmation parameter. The const value 'SUBMIT_AGENT_PACK' is largely self-explanatory, so this is adequate but not strong.

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 opens with a specific verb 'Move' and identifies the exact resource ('complete owner-bound draft') and destination ('immutable Agent AVCP queue'). It also names a readiness gate, which separates it from nearby siblings like create_ai_pack_draft, publish_ai_pack, and run_ai_pack_audition.

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 explicit precondition for when the tool is appropriate, while 'owner-bound draft' defines the eligible input. It does not explicitly list when-not-to-use alternatives, but the readiness condition and sibling set make the intended workflow clear.

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

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

Annotations already indicate a non-read-only, non-destructive operation, and the description adds meaningful behavioral context by specifying that the updatable data is 'seller-owned public metadata' and that 'immutable pack versions are never overwritten'. This directly reassures agents that version content is safe from modification, going beyond what the annotations alone convey. No contradiction with annotations exists.

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

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

The description is a single, front-loaded sentence with no filler: it states the action and resource first, then adds the critical immutability constraint. This is appropriately compact for a tool whose schema already carries most structural detail. A small expansion covering parameter behavior or usage routing would still fit comfortably without harming 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?

This is a complex update tool with eight top-level parameters, a deeply nested listing object, no output schema, and minimal annotations, yet the description only provides scope and immutability. It does not clarify whether updates are partial or full replacement, how the required productId relates to ownership, what validation constraints apply, or what the response/result of the update looks like. Agents are left with substantial uncertainty about how to invoke 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 description coverage is only 13%, and the description does not compensate by explaining any of the eight parameters or the nested listing object. The generic term 'public metadata' gives a high-level category but adds no field-level meaning beyond what property names like tags, models, priceUsd, or shortDescription already suggest. Given the low coverage, the description should have clarified at least the key editable fields or the update semantics.

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

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

The description uses a specific verb ('Update') and identifies the resource as 'seller-owned public metadata', making it clear this is a metadata-editing operation rather than a version upload or publish action. The phrase 'immutable pack versions are never overwritten' further distinguishes it from operations that modify the pack content itself. However, the description does not explicitly tie it to the 'draft' lifecycle stage in the tool name, and 'listing details' in the title vs 'public metadata' in the description introduces slight ambiguity.

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 explicit guidance about when to use this tool versus alternatives such as upload_ai_pack, publish_ai_pack, or create_ai_pack_draft. The statement about immutable pack versions implies this tool is for metadata-only updates and not for changing pack content, but the implication is left for the agent to infer. With a large sibling set, explicit routing or exclusions would have been valuable.

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

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

Annotations already signal this is a write (readOnlyHint=false), and the description adds meaningful behavioral detail: the upload is product-scoped, the resulting version is immutable, and the file is read from a local path. It stops short of describing failure modes or preconditions like authentication, but it adds value beyond the annotations.

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

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

The description is a single dense sentence with no filler: it front-loads the explicit path requirement, then the transfer mechanism, then the commit outcome. Every clause adds information an agent needs.

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 workflow is clear, but there is no output schema and the description omits return value, version-conflict behavior, and error/precondition details. This is adequate for a straightforward upload/commit call, yet it leaves meaningful gaps for autonomous selection and invocation.

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

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

With only 25% schema description coverage, the description compensates: packPath is the explicit local .taip path, version is the immutable higher version to commit, and productId is the product scope of the private upload contract. Only the optional changelog parameter is left to inference from its name.

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 resource (an explicit local .taip path) and a precise operation: transfer through the private upload contract and commit an immutable higher version. This clearly separates the tool from siblings like upload_ai_pack_image, update_ai_pack_draft, and publish_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?

The description gives a clear invocation context: use it when an explicit local .taip pack exists and a higher immutable version should be committed. It does not name alternatives or explicitly state when-not-to-use, but the scoping is strong enough that an agent can identify the intended case.

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 sparse (not read-only, not destructive), and the description adds useful constraints: local file, supported formats, and seller-owned pack listing. However, it does not disclose whether an existing preview image is overwritten, whether ownership is validated, or what the tool returns. No contradiction with annotations.

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

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

A single sentence front-loads the action and includes only essential constraints. No filler, repetition, or unnecessary detail.

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?

Adequate for a simple two-parameter upload tool if the caller already understands ownership and intended placement, but it does not explain overwrite behavior, authentication requirements, or the response/return value. With no output schema, the missing return semantics are a notable 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?

The schema only documents productId; imagePath has no description. The description adds meaning by clarifying that imagePath is a local image path rather than a URL and lists accepted formats, but it omits path style, size limits, and how the image is tied to the productId.

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 verb (Upload), a precise resource (one local image to the seller-owned pack listing), and the allowed formats (JPEG, PNG, WebP, AVIF). The tool is clearly differentiated from the sibling upload_ai_pack by the word 'image' and the 'preview image' framing in the title.

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 on when to use this tool versus upload_ai_pack, update_ai_pack_draft, or publish_ai_pack. The seller-owned listing condition is mentioned but not explained, and no alternatives or exclusions are provided.

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