PreVibe

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

Annotations already mark idempotentHint true, openWorldHint true, and readOnlyHint false. The description adds meaningful behavior beyond annotations: it is free, only re-checks names still marked 'not checked,' and returns a run unchanged if all names are resolved. This aligns with and enriches the idempotency annotation without contradiction.

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

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

The description is two sentences with no filler. The main action is front-loaded, followed by cost and idempotency conditions. Every clause adds useful information, making it both concise and complete for the 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 single-parameter tool with no output schema, the description covers purpose, cost, and behavioral edge cases. It could mention the return type or possible external side effects (e.g., live registrar queries), but the idempotency hint and openWorldHint supplement enough 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 schema covers the single 'id' parameter fully with 'The brand-name run ID to backfill availability for.' The description reinforces the run context but adds no new parameter-level details such as format or examples. With 100% 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 and resource: 'Fill in domain availability and prices for names in a run that were saved without them.' It explains the context (registrar throttled at generation time), which makes the tool's purpose concrete and distinct from siblings like get_brand_names or check_credits.

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

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

The description implies when to use the tool: after a run has names without availability due to throttling. It also clarifies that only unresolved names are re-checked, so an agent can decide whether calling it will have effect. It does not explicitly name alternatives, but no sibling does the same thing, so the context is sufficient.

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

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

The annotations already declare readOnlyHint=true, and the description's 'Check' wording is consistent with that. The description adds useful context beyond the annotations by specifying that the balance is 'current' and tied to the 'authenticated user,' implying a live account-scoped read with an authentication precondition.

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 states the action, the resource, and the scope without wasted words. 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 zero-parameter read-only balance check, the description is nearly complete: it identifies what is being checked, whose balance it is, and that it reflects the current state. It does not describe the return format, but the tool's simplicity and read-only annotation make this a minor omission rather than a blocking 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 tool has zero parameters, so the baseline of 4 applies. There is no parameter meaning for the description to add, and the input schema already fully covers this by having an empty properties object.

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 ('Check') and a specific resource ('current credit balance') scoped to the authenticated user. It is immediately distinguishable from the sibling tools, which all deal with brand names, deep dives, or research rather than account credits.

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 establishes the use case: checking the authenticated user's current credit balance. It does not explicitly name alternatives or exclusions, but no sibling tool covers credits, so the intended context is unambiguous and no wrong sibling could plausibly be selected.

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

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

Beyond annotations, the description adds meaningful behavioral context: it costs 1 credit, targets either the founder's idea or a top bet, and returns a table with domain, availability, price, and companion domains. It does not fully describe side effects or persistence, but the annotations already indicate the operation is not read-only.

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 and front-loaded: core action first, then target semantics, then cost and output. Every sentence carries useful information with no 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 two-parameter tool with no output schema, the description is complete enough for an agent to invoke correctly. It covers prerequisites, parameter defaults and options, cost, and the structure of the returned table.

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 for parameters is 100%, so the baseline is 3. The description adds value by explaining the default target ('idea'), what bet-1/bet-2/bet-3 mean, and the requirement that research be complete, going beyond the schema descriptions.

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

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

The description clearly states the verb and resource: 'Generate a set of brandable names for a completed research.' It also distinguishes the tool from siblings like listing, deleting, or suggesting more names by emphasizing grounded positioning and domain-availability checking.

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 usage context: requires the parent research to be complete and explains how the target parameter selects what to name. It does not explicitly name alternatives or exclusion conditions, so it stops short of a 5, but an agent can infer the correct use 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?

The description goes beyond the all-false annotations by disclosing that the operation costs 1 credit, that it strictly uses existing research rather than new market data, and that parent research must be complete. It also notes that it returns the full analysis, which is valuable given there is no output schema.

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

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

The description is dense but every sentence earns its place: purpose, source constraint, parameter clarification, prerequisite, cost, and return value. The most important information is front-loaded, and there is no repetition of schema fields.

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 output schema and moderate complexity, the description covers the essential operational context: what it produces, what it requires, how it is sourced, what it costs, and how the target parameter behaves. Nothing critical is missing for an agent to decide whether to 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 100%, so the schema already documents both parameters well. The description reinforces the 'target' semantics and the omit-default behavior, but adds little beyond the schema's own detailed descriptions. A 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 starts with a specific verb and resource: 'Generate an in-depth framework analysis' for a completed research. It enumerates concrete components (jobs-to-be-done, positioning, MVP scope, Kano, pricing, 90-day plan, risks) and clearly distinguishes itself from research creation by requiring the parent research to be 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 description clearly states when to use the tool: only after research is complete, and only seeded from that research without new market data. It does not explicitly name alternatives like retry_deep_dive or create_research, but the precondition 'Requires the parent research to be complete' provides clear contextual 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?

Beyond the annotations, it discloses a concrete side effect: 'Costs 1 credit.' It also reveals that the response is only a compact summary with verdict badge, score, findings, top bets, and ID/URL, and that the full report must be fetched separately. No contradiction with 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?

Three dense sentences front-load the main purpose, then cover input constraints, cost, output shape, and follow-up retrieval. Every clause carries useful information with no padding.

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 an output schema, this description fully explains input, cost, and the summary-vs-full-report behavior, including the get_research follow-up. It is slightly incomplete only in not differentiating create_research from create_deep_dive within the sibling tool set.

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 the schema already describes the idea parameter as 'anything from a one-liner to full multiline notes.' The description repeats this and adds the 4,000-character limit already present in the schema's maxLength, so it contributes little beyond the schema.

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

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

The description clearly states the action and resource: 'Generate a comprehensive market research report based on a business idea.' It differentiates from get_research by noting that this call returns only a summary, but it does not explicitly distinguish create_research from the sibling create_deep_dive.

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 context for when to call: when a business idea needs market research, and it accepts anything from a one-liner to full notes. It also points the agent to get_research for fetching the full report later, but it does not explain when create_deep_dive or retry_research might be the better alternative.

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 this as destructive and non-read-only. The description adds valuable behavioral details: the action cannot be undone, it is free, and it is restricted to runs whose parent research the authenticated user owns. This 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 three short sentences with no filler. The core action is front-loaded, followed by cost, ownership, and irreversibility constraints. Every sentence adds information the 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?

For a single-parameter delete operation with annotations already covering destructiveness and idempotency, the description is largely complete. It covers ownership, irreversibility, and cost. It could mention the response or behavior when the id does not exist or is not owned, but this is not critical for invoking the 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 the only parameter, id, is already described as 'The brand-name run ID to delete.' The description adds no deeper semantics beyond restating that deletion is by id, so the schema carries the burden.

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 and resource: 'Delete a brand-name run by id.' This clearly differentiates it from sibling tools like delete_research and delete_deep_dive, and clarifies that it targets a run rather than the brand names themselves.

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: the action is free, irreversible, and only permitted when the authenticated user owns the parent research. It does not explicitly name alternative tools or state when not to use this tool, but the ownership constraint and resource specificity give sufficient guidance for selecting 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?

Annotations already declare destructiveHint and idempotentHint, and the description adds meaningful behavioral context: the action cannot be undone, and it is restricted to deep dives whose parent research the authenticated user owns. This goes beyond the annotations and helps the agent anticipate side effects and authorization failure. No contradiction found.

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 short and front-loaded with the core action. Every sentence carries meaningful information except perhaps the standalone 'Free.', which is an odd addition but not harmful. The structure is efficient and easy 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?

For a simple single-parameter delete operation, the description covers the target, the authorization constraint, and irreversibility. It does not explain error behavior or response format, but the output schema is absent and annotations cover idempotence. Overall, it is sufficiently complete 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 schema has 100% description coverage for the single parameter, and the description simply restates that deletion is by ID. The schema already documents the format, requiredness, and meaning of 'id', so the description adds minimal additional semantic value. 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 action ('Delete'), a specific resource ('deep dive'), and the selection mechanism ('by ID'). It also adds an ownership qualifier that distinguishes this from deleting other resources like research or brand names. The resource is unambiguous even without reading sibling names.

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

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

The description implies this tool is for deleting deep dives and explicitly states an important prerequisite: the authenticated user must own the parent research. However, it does not explicitly mention alternatives or when NOT to use this tool. The ownership and irreversibility constraints give context but not full usage 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?

Beyond the destructiveHint and readOnlyHint annotations, the description adds critical behavioral details: the operation is irreversible, it cascades to linked deep dives, and it is restricted to owned research. This is exactly the kind of context an agent needs before invoking a destructive 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 compact and front-loaded: the core action appears first, followed by necessary caveats. Every sentence adds value, covering cost, ownership, cascade behavior, and irreversibility without 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 one-parameter delete operation with no output schema, the description is complete. It covers what is deleted, who can delete it, what else gets deleted, and that the action cannot be undone.

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

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

The schema already fully documents the single `id` parameter with type, format, pattern, and a description. The tool description adds little parameter-specific meaning beyond saying 'by ID,' so the baseline score 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, 'Delete a specific research by ID,' with a clear resource and identifier. It distinguishes itself from sibling tools like delete_deep_dive and delete_brand_names by naming 'research' as the target resource.

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 usage context: it can only delete research owned by the authenticated user, and it mentions the cascade behavior for linked deep dives. It does not explicitly name alternatives or say when not to use it, but the ownership restriction effectively defines a boundary.

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 establish readOnlyHint=true and openWorldHint=false, so there is no contradiction. The description adds value by disclosing the retrieval scope: all names with per-name domain, availability, price, and companion/TLD domains — useful context not inferable from annotations or schema alone.

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 states the action and then the key output details after a colon. Every element earns its place and there is no 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 low-complexity read-only tool with one required parameter and no output schema, the description explains the expected return content well enough to invoke correctly. It does not detail exact field formats or error behavior, but the absence of an output schema makes the provided output summary reasonably 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?

Schema description coverage is 100% for the single id parameter, including format and description ('The brand-name run ID'). The tool description repeats 'by id' but adds no parameter semantics beyond the schema, so it sits at the baseline.

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

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

The description uses a specific verb and resource — 'Get one brand-name run by id' — and enumerates what is included: names, domain, availability, price, and companion/TLD domain variants. This clearly distinguishes it from list_brand_names, create_brand_names, and delete_brand_names.

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 'by id' phrasing establishes the clear context: use this when you already have a run id and need one run's full name details. It does not explicitly cite alternatives like list_brand_names, but the usage context is unambiguous and no exclusions are needed.

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 provide readOnlyHint=true, so the safe read behavior is known. The description adds the useful detail that the full analysis text is returned, but it does not mention behavior such as 404 handling or response shape. This is acceptable given annotations but not richly transparent.

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

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

The description is one compact sentence that front-loads the core action and resource, then adds the key return detail. 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 single-parameter read operation with a clear ID schema and readOnlyHint annotation, the description is complete. It states what is returned ('full analysis text') and the retrieval criterion, which is sufficient for an agent to 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?

The schema already fully documents the single 'id' parameter with a UUID format and description 'The deep dive ID', giving 100% schema coverage. The description only restates that the lookup is by ID, adding no new parameter-level meaning.

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

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

The description clearly states the action ('Get'), the specific resource ('a specific deep dive by ID'), and what is included ('full analysis text'). This distinguishes it from list_deep_dives, which would return multiple items, and from create/delete/research 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 phrase 'by ID' clearly signals this tool is for fetching one known deep dive rather than enumerating all deep dives. It does not explicitly name alternatives like list_deep_dives, but the context is sufficiently clear for an agent to select it appropriately.

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 openWorldHint=false, so the safety profile is covered. The description adds that the tool returns 'full results,' giving some sense of response richness, but it does not describe error behavior, missing-ID handling, or response structure. With annotations carrying the read-only burden, 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?

One focused sentence with no redundancy or filler. The verb, object, and distinguishing detail ('by ID') are front-loaded, and nothing is repeated from the title 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?

For a simple one-parameter read-only fetch, the description provides enough to select and invoke the tool correctly. The main gap is that 'full results' is vague and there is no output schema, so an agent cannot anticipate the exact return shape; however, the low complexity makes this a minor omission.

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 id parameter as 'The research ID' with a strict UUID format, and schema description coverage is 100%. The tool description adds no extra parameter-level meaning, so the 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 clearly states a specific verb ('Get'), resource ('research'), and selection criterion ('by ID'), and mentions 'full results' so an agent can distinguish this from list_researches and other entity-specific getters. It is immediately actionable.

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 'by ID' phrasing implies this tool is for targeted retrieval once an ID is already known, which is a clear contextual signal. However, it does not explicitly mention when not to use it or direct the agent to sibling tools such as list_researches for finding IDs or get_deep_dive for other entity types.

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 establish the operation as read-only, and the description adds the return shape (a table of runs with id, target, created, name count, and status). It does not disclose potential limits such as pagination, ordering, or behavior when no runs exist, but for a simple read-only list this is a reasonable level of transparency.

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

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

The description is two sentences with no filler. The core action and scope are front-loaded, the parenthetical clarifies an important characteristic, and the return fields are listed compactly.

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 a single parameter, a read-only annotation, and no output schema, the description adequately covers the essential context by listing the returned fields. Minor ambiguity remains around 'name count' and possible empty-result behavior, but this is a low-complexity tool and the description is reasonably complete.

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

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

The single parameter researchId is fully described in the schema with type, format, pattern, and a clear description. The tool description adds no additional parameter semantics beyond what the schema already provides, so the baseline score 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 uses a specific verb ('List') and resource ('all brand-name runs generated for a specific research'), and explicitly notes that a research can hold several runs. This clearly differentiates it from sibling tools like get_brand_names or create_brand_names by emphasizing the plural, list-oriented nature of the operation.

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 makes it clear that the tool lists runs for a single research, which implies when to use it. However, it does not explicitly contrast it with alternatives such as get_brand_names or list_researches, nor does it state 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?

Annotations already provide readOnlyHint=true and openWorldHint=false, covering the safety profile. The description adds behavioral context by stating the return format ('Returns a formatted table') and scoping to generated deep dives, going beyond what annotations alone convey.

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 filler. The primary action and scope are front-loaded, and the return-format detail is added as a separate short sentence. 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 list operation with one fully documented parameter and read-only annotations, the description is nearly complete. It covers what is listed and the output format, though it does not specify table contents, ordering, or empty-result behavior, which would have made 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?

The input schema provides 100% coverage with a clear description of the researchId parameter ('The research ID whose deep dives to list'). The tool description reinforces this but adds no extra parameter-level semantics, so 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 states a specific verb ('List') and resource ('all deep dives generated for a specific research'), making the operation clear. The scope is explicit and distinct from sibling tools like get_deep_dive (single item), create_deep_dive, and delete_deep_dive.

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

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

The description implies when to use the tool: when you need to enumerate deep dives tied to a researchId. However, it does not explicitly state when not to use it or compare it to alternatives such as get_deep_dive or list_researches, so usage guidance is only implied.

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 annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds useful behavioral context beyond that by stating that the result is returned as a formatted table, which helps the agent understand the output style. It does not disclose ordering or pagination semantics, but those are less critical given the simple list 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 short sentences with no filler. The core purpose is front-loaded, and the output format detail is useful without bloating the description.

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 list tool with two optional parameters and no output schema, the description covers the essential facts: what is listed, whose data is returned, pagination support, and the output format. Slightly more detail about the returned table's columns or ordering would improve completeness, but it is adequate as-is.

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

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

The input schema has 100% description coverage for both limit and offset, so the parameters are already fully documented there. The description only adds a general mention of pagination support and provides no additional parameter-level meaning, 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 clearly states the action (list), the resource (researches), and the scope (authenticated user), with pagination and return format noted. It does not explicitly distinguish itself from sibling list_* tools, but the resource name makes the distinction obvious.

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 tool to enumerate the authenticated user's researches, with pagination support. It does not mention when not to use it or name alternatives, but for a straightforward read-only list operation this is not a significant gap.

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 all false, so the description carries the behavioral burden. It usefully discloses that the retry is free because the credit was already charged, and that it returns the full regenerated analysis. It does not fully clarify whether the retry modifies the existing deep dive or creates a new one, but the provided behavior is still materially helpful.

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

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

Two sentences deliver the core purpose, the trigger condition, the cost behavior, and the return value with no filler. The most decision-relevant information is front-loaded.

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

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

With one documented parameter, a clear condition, cost disclosure, and a stated return value, the description is complete for this low-complexity tool. The absence of an output schema is adequately compensated by 'Returns the full regenerated analysis.'

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

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

The schema already documents the single id parameter with 100% coverage, so the baseline is 3. The description reinforces that the ID refers to a deep dive whose generation did not complete, but it does not add new parameter detail beyond the schema.

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

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

The description states a specific verb (retry), a specific resource (deep dive), and the precise condition under which it applies (generation did not complete: failed or stuck without a result). It also distinguishes the tool from the sibling retry_research by naming the deep dive resource explicitly.

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 tells when to use the tool: when a deep dive generation failed or is stuck without a result. It does not explicitly name retry_research as the alternative for research retries, but the resource distinction is apparent enough that an agent can infer the correct use 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?

The description adds valuable behavioral context beyond the annotations: it is free because credit was already charged, it returns the compact summary shape of create_research, and it points to get_research for full reports. These details do not contradict 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?

Two sentences, front-loaded with the core purpose and then covering cost and output routing. Every sentence earns its place with no redundant wording.

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 retry operation, the description covers the triggering status, cost behavior, return summary, and follow-up path to get_research. Combined with the output schema and annotations, nothing essential is missing 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 input schema already describes the single id parameter with 100% coverage, including format and a clear description. The tool description adds no additional parameter-level meaning, which is acceptable but not enriching.

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 ('retry') on a specific resource ('research') and scopes it to research whose generation failed or is stuck. It also distinguishes the tool from siblings by noting its return matches create_research and that full reports come from get_research.

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 defines when to use the tool ('status failed, or stuck without a result'), clarifies that no additional credit is charged, and directs agents to get_research when a full report is needed. This gives clear decision guidance versus alternative tools.

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

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

The description goes well beyond the annotations by disclosing deduplication ('names not already shown'), non-destructiveness ('nothing is removed', consistent with destructiveHint=false), zero cost ('Free'), a rate limit ('capped at a few rounds per run'), a way to know the remaining rounds, and the return shape ('Returns the run with its new names'). This is rich behavioral context for an agent. No contradiction with annotations found.

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 core action is front-loaded in the first sentence, and every sentence contributes useful information (purpose/dedup, cost, cap, return). The cap sentence is slightly verbose ('so you never have to hit the cap to find out') and could be tightened, but overall the description is efficient and well-ordered.

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 parameter, no output schema, and annotations present, the description covers all essential aspects: purpose, dedup behavior, non-destructiveness, cost, rate-limit handling, and return value. There is no output schema, so the description's explanation of 'Returns the run with its new names' is critical and provided. Nothing an agent needs to call it correctly 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?

Schema description coverage is 100% — the single `id` parameter is already documented as 'The brand-name run ID to extend', which fully conveys its meaning. The description's phrase 'existing brand-name run' reinforces this but adds no new syntax or format details, so the baseline 3 for high schema coverage 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 ('append') and resource ('an existing brand-name run'), and clarifies the scope ('a fresh batch', 'names not already shown'). It clearly distinguishes itself from create_brand_names (which starts a new run) and get/list_brand_names (which read), so an agent can route correctly without opening sibling schemas.

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 'existing brand-name run' clearly signals this tool is for extending an already-created run, which implicitly excludes create_brand_names as the alternative for starting fresh. However, it does not explicitly name sibling alternatives or state 'use X when you need to start a new run', so the guidance is 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.