Vela Breakout

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

No annotations are provided, so the description carries the full disclosure burden. It usefully adds that answers come with checked figures and chart links, and that charts render inline where the client supports it, but says nothing about data freshness, coverage limits, or failure behavior for unsupported questions.

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 short sentences with the core purpose front-loaded; the inline-chart note is secondary detail but still earns its place. No filler or repetition.

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 does the right thing by sketching the return shape (figures plus chart links, inline charts). For a one-parameter tool it is close to complete, missing only data-scope and failure-mode 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 coverage is 100% for the single `question` parameter, so the baseline is 3. The description adds only domain scoping (the question should concern tech growth), which is modest added meaning beyond the schema's "The question in plain words".

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 concrete verb+resource: answering questions about what is growing in tech, with checked figures and chart links. This is clear enough to distinguish it as the broad Q&A entry point, though it never explains how it differs from topic-specific siblings like research_ideas or rising_companies.

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?

"Best first call" gives a clear routing context — reach for this before the narrower siblings — but no explicit when-not conditions or named alternatives are provided, so the agent must infer the routing 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?

With no annotations, the description carries the full behavioral burden. It usefully discloses that results are scoped 'over a window' and what each group actually measures (tokens, installs, downloads, open jobs, points), which is real semantic context. It never defines how 'fastest-growing' is computed, the ordering/ranking of the returned items, or result format, which are significant gaps for a growth-ranking 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?

Purpose is front-loaded in the first clause, followed by the group mapping, so the reader gets the gist immediately. The group list is long but informationally dense and arguably necessary given opaque enum ids. Minor deduction for packing all 16 groups into one run-on sentence rather than a structured list.

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?

There is no output schema and no annotations, so the description must stand alone. It covers groups and window behavior well, but omits what a 'breakout' result actually looks like (ranked items, growth figures, direction) and how growth is measured. For a ranking tool with 5 parameters, that leaves an agent under-informed about outcomes.

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

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

Schema coverage is 80%, setting a baseline of 3, and the description exceeds that by decoding the opaque group enum values with their underlying metrics — value the bare enum in the schema does not provide. It adds no detail for limit, steady, min_size, or window_days, but those already carry 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 states a specific action and scope: 'the fastest-growing items of one group over a window,' which an agent can distinguish from a broad search. The per-group metric annotations (e.g., 'ai_models: tokens a week', 'company_buzz: points in 12 months') make the resource concrete. It stops short of naming how it differs from the sibling rising_companies, leaving that inference to the agent.

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

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

Usage is only implied: the tool requires a group id, so an agent can infer it is for scoped growth lookups after listing groups. There is no explicit when-to-use, when-not-to-use, or named alternative (e.g., when to prefer rising_companies or research_ideas). The 'one group' framing hints at the boundary but does not draw 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?

No annotations are provided, so the description carries the full behavioral burden. It does disclose the shape of the returned data (growth, peer comparison, page), which is useful context, but says nothing about read-only safety, auth/permission needs, rate limits, or whether a missing company errors or returns empty.

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 tight sentence with the scope front-loaded, no filler. The trailing 'and its page' is slightly ambiguous but costs little.

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

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

With no output schema and no annotations, the description must cover return values, which it partially does. For a simple one-parameter read tool this is roughly adequate, but the absence of routing guidance and any error/behavior notes leaves gaps.

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

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

Schema coverage is 100% and there is a single parameter whose description already documents format and example ('supabase.com'), so the schema does the heavy lifting. The description adds no syntax or constraint details beyond that 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 states a clear resource (one company) and enumerates what is returned: per-signal growth, peer comparison, and page. The retrieval verb is only implied by the name rather than stated, and no sibling is named, but an agent can tell this is the single-company lookup distinct from list_groups or rising_companies.

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 when-to-use or when-not-to-use guidance and no alternatives referenced, even though siblings like find_breakouts and rising_companies cover overlapping territory. The agent must infer the routing from the tool name alone.

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

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

With no annotations, the description carries the full burden. It helpfully discloses return contents (unit, caveat, usual window) but omits whether the tool is read-only, takes no arguments, paginates, or produces any side effects. Output structure is only partially described.

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?

It is a single, front-loaded sentence with no wasted words. The core resource (groups) and returned metadata are delivered immediately.

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

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

For a simple zero-parameter discovery tool, the description covers the returned fields but leaves the meaning of a 'group' and how to consume the results undefined. There is no output schema to compensate, and the tool's role relative to siblings is not established.

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 takes zero parameters, so the baseline score is 4. The description correctly does not discuss parameters, matching the empty input schema.

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

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

The description states it returns every available group along with metadata (unit, caveat, usual window), which is clear enough to identify it as a catalog/list tool. However, it does not use an explicit verb and does not distinguish itself from sibling tools, which are all unrelated.

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

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

There is no guidance on when to call this tool versus alternatives, no prerequisites, and no mention of related tools such as 'ask'. The only implied usage is that it enumerates available groups, which is minimal.

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

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

With no annotations, the description carries the full burden. It does disclose genuinely useful behavior – that ranking is by change in share of all papers over a defined 3-vs-3-month window – which is the key non-obvious trait. But it says nothing about ordering, result volume, rate limits, or permissions, so significant ground is uncovered.

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 with the metric and comparison window front-loaded and no filler. Nothing could be trimmed without losing 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?

There is no output schema, so return values need not be explained, and the metric definition is the hardest part to convey – the description does that well. However, with zero parameter documentation and no annotations, an agent still lacks enough to choose direction values or a sensible limit.

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

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

Schema description coverage is 0%, so the description must compensate for both parameters and does not. The 'rising/fading' intent in the title loosely maps to the direction enum, but the enum values are never named, and the limit parameter (1-25) is not mentioned at all.

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 resource (AI research ideas) and the exact metric/scope it is ranked by: change in share of all papers, last 3 months vs the prior 3. An agent can tell what it returns. It does not, however, differentiate itself from conceptually adjacent siblings like find_breakouts or rising_companies.

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 when-to-use or when-not-to-use statement, and no alternative tool is named. The direction enum and the title hint that this is for spotting rising/fading topics, but that is inference rather than guidance.

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

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

With no annotations, the description carries the full behavioral burden, and it only discloses that results are ranked by magnitude. It says nothing about what a 'measure' is, the direction of ranking, whether this is a live scan or cached data, or any rate/permission constraints.

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

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

A single efficient sentence with the resource and ranking behavior front-loaded and no wasted words. The compactness is appropriate, though part of the brevity comes at the cost of clarity rather than being purely economical.

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 annotations and no output schema, the description is too thin: the core concept ('measures', 'how much') is never defined and no return shape is hinted at. An agent could not confidently predict what comes back.

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

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

Schema description coverage is 0% and the single parameter (limit, max 25) is not mentioned in the description at all. The max-25 cap and default behavior are left entirely for the agent to infer, so the description does not compensate for the coverage gap.

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?

'Companies breaking out on two or more measures, ranked by how much' states a resource (companies) and a rough action, but 'measures' and 'how much' are undefined jargon. It does not distinguish itself from the sibling find_breakouts, whose name suggests near-identical behavior, leaving the agent unable to tell them apart.

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 when-to-use statement, no prerequisites, and no mention of alternatives. The presence of a sibling literally named find_breakouts makes this omission material, since the two tools are not differentiated anywhere in the text.

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