Weav Customer Service

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

With no annotations, the description discloses that the tool returns short structured content and a URL, which is the core behavior. However, it does not say whether the comparison is static, live, or updated, nor does it mention any side effects; for a read-only get_ tool these gaps are minor.

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

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

A single sentence that front-loads the action and the deliverable, with no wasted words or redundant schema 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?

For a simple no-input lookup with no output schema, the description is nearly complete: it states what is returned and that a URL is included. It only lacks explicit guidance on when choosing comparison over pricing/demo/overview, which is a small 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 and schema coverage is 100%, so there are no parameter semantics for the description to add. The baseline for a zero-parameter tool 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?

Description names a specific verb ('Return') and a concrete resource: a structured contrast of Weav against three named competitors plus the comparison page URL. This clearly distinguishes it from sibling tools like get_pricing or get_product_overview.

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 obvious it is for comparison requests against a fixed competitor set, but it does not explicitly state when not to use it or name alternatives. For a no-parameter lookup tool this implied context is nearly sufficient, though explicit routing would be stronger.

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

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

With no annotations provided, the description carries the full behavioral burden. It explicitly states the tool 'Does not collect email or other PII' and returns a URL for a human or agent to open, disclosing both the action and an important non-effect. It doesn't detail all possible behaviors, but for this simple tool it is sufficiently 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 a single, tightly worded sentence that leads with the core return value and then adds a clarifying behavioral note. Every part earns its place, with no redundancy or filler.

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

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

Given the tool's extreme simplicity—zero parameters, no output schema, no annotations—the description fully covers what the agent needs to know: what the tool returns and a key non-side-effect. 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 and the schema is an empty object, so parameter semantics are not applicable. The baseline of 4 applies because there is nothing for the description to add beyond what the schema already communicates.

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 specific resource ('the sales/demo booking URL (weav.com/sales)'), which clearly distinguishes it from siblings like get_pricing or get_signup. There is no ambiguity about what the tool provides.

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

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

It implies the use case: when you need the demo booking URL and want to open it directly. It also clarifies that no PII collection is involved, which sets expectations for the agent. It does not explicitly name alternative tools or say when not to use it, but for a zero-parameter URL getter this is adequate.

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

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

With no annotations provided, the description carries the behavioral disclosure burden. It transparently states it 'Return[s]' data and specifies that the data is 'public pricing,' implying a read-only, non-sensitive operation. It could add details about data freshness or failure modes, but for a simple zero-parameter lookup this is sufficient.

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, information-dense sentence followed by one actionable directive. It front-loads the core purpose and lists all specifics without wasted words.

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

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

For a zero-parameter, no-output-schema tool, the description tells the agent everything needed to call it: the exact data points to expect and why to use it. It doesn't address currency, regional variation, or update cadence, but these are minor gaps for a public pricing endpoint.

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 no parameters, so the schema is empty and there is no parameter documentation burden. The baseline of 4 for zero-parameter tools applies; the description accurately describes what will be returned without needing to explain parameter meanings.

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

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

The description states a specific verb ('Return'), a clear resource ('Weav public pricing'), and enumerates exact contents (Lite, Plus, Pro, Max monthly prices, annual discount, add-ons, links). This clearly distinguishes it from sibling tools like get_comparison or get_signup, which focus on different aspects.

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 a clear directive: 'Use this instead of scraping weav.com/pricing,' which tells the agent when this tool is the right choice versus an external approach. However, it doesn't explicitly mention sibling alternatives or conditions for choosing them, so it stops short of full when/when-not 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?

No annotations are provided, so the description must carry the burden. It discloses that the tool returns a structured overview and includes links to product and docs. For a zero-parameter read-only tool, this is adequate 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?

One sentence front-loads the purpose and lists the relevant content areas with no filler. Every element 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 no-parameter tool with no output schema, the description is complete: it specifies what the overview covers and notes that product/docs links are included. An agent can select and invoke this tool with confidence.

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

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

There are no parameters, so parameter-specific description is unnecessary. The baseline of 4 applies because the tool's behavior fully compensates for the absence of parameter details.

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

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

The description states a specific verb ('Return') and resource ('structured overview of Weav'), then enumerates the covered topics. This clearly distinguishes it from siblings like get_comparison and get_pricing.

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 clear when to use it: when an agent needs a broad structured overview of Weav's main capabilities. It does not explicitly name alternatives or exclusions, but the scope is specific enough to guide 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?

There are no annotations, so the description carries the full disclosure burden. 'Return' indicates a read-only retrieval and the payload is specified, but the description does not explicitly rule out side effects, mention response format, or note any access requirements. It 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 front-loaded sentence with no filler. It states exactly what the tool returns and includes the URL, making it easy for an agent to parse quickly.

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 tool with no output schema and no annotations, the description gives enough information to select and invoke it: the return value is summarized as the signup URL plus Lite-start inclusions. It could go further by enumerating those inclusions or specifying the response shape, but it is essentially complete for this simple 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 tool has zero parameters, so the schema already exhaustively covers parameter semantics. The description does not need to add parameter-level meaning; per rubric, 0 params merits a baseline of 4.

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

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

The description uses a specific verb ('Return') and names a concrete resource: the self-serve signup URL (app.weav.com/register) plus what the free Lite start includes. This clearly distinguishes the tool from siblings focused on comparison, demo, pricing, and product overview.

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 intended use is implied: call this when a user asks for the signup URL or details about the free Lite plan. However, the description does not explicitly state when not to use it or point to siblings such as get_demo or get_pricing, so usage guidance is only implicit.

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