Weav Customer Service

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

Annotations already declare the tool read-only, idempotent, and non-destructive, so the safety profile is covered. The description adds the return shape at a high level (structured contrast plus URL) but does not go beyond annotations with details like caching, rate limits, or the exact structure of the contrast.

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

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

The description is a single well-structured sentence with no filler. The main deliverable is front-loaded, and the additional return element (comparison page URL) is appended naturally.

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 retrieval tool, the combination of full schema documentation, clear annotations, and a concise description is largely sufficient. The only minor gap is that no output schema exists and the description does not detail the exact structure of the 'short structured contrast,' leaving some ambiguity about the return format.

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 fully documents all three parameters. The description adds no parameter-level meaning, which is acceptable under the baseline, but it also does nothing to further clarify how the parameters affect the result.

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 a specific resource: a structured contrast of Weav vs Ada, Intercom, and Zendesk, 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 intended use is clear from the description: an agent should call this when the user wants to compare Weav against those named competitors. It does not explicitly mention when not to use it or name alternative tools, but the context is unambiguous enough for basic 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, indicating a safe, non-mutating operation. The description adds value by specifying that no PII is collected and the URL is returned for the human/agent to open, which clarifies side effects beyond the annotations. It does not contradict annotations, as the tool is purely informational.

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 long, with the core purpose front-loaded and the PII caveat following. Every sentence adds value without redundancy, making it efficient and direct.

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 an informational, read-only tool with 100% schema coverage and no output schema, the description is almost sufficient. It covers the main function and PII behavior. However, it could mention that the URL is external or that the agent should open it, but the current wording ('for the human or agent to open') already implies this, so no major 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 description coverage is 100%, meaning both required parameters (context, llm_model) are documented with clear purpose in the schema, so the description does not need to elaborate. The optional conversation_id is also described in the schema. The description adds no extra parameter meaning but does not need to, given the 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 states the tool returns a specific URL (weav.com/sales) and explicitly notes it does not collect email or PII, which distinguishes it from signup or similar tools. It mentions the resource (sales/demo booking URL) and the action (return), 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 indicates the tool is for returning the booking URL without collecting PII, which implies usage when a sales/demo booking link is needed. It does not explicitly name alternatives like get_signup or get_pricing, but the context of sales/demo booking gives clear usage guidance for typical queries.

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 communicate read-only, idempotent, open-world, and non-destructive behavior, so the description does not need to restate those. The description adds useful context about when to invoke the tool, but it does not disclose return format, rate limits, or other runtime behavior. 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?

A single front-loaded sentence conveys both the purpose and the usage threshold without filler. Every clause adds value, and the fallback clause is a meaningful decision heuristic for the 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?

For a meta-tool with rich parameter documentation and safe annotations, the description is largely complete: it tells the agent when to call and the schema tells it how. The lack of an output schema means return structure is not described, but 'additional tools' reasonably implies a list of tool definitions, so this is a minor gap rather than a blocking 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?

Schema description coverage is 100%, and the schema itself provides detailed guidance: llm_model is for analytics only and must not be guessed, and conversation_id must come from a prior server response without parallel calls until available. The tool description adds no parameter-specific meaning, so the baseline score 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 that the tool checks for additional tools and gives the trigger condition: when specialized capabilities might help. This immediately distinguishes it from the domain-specific getters and search tools among its siblings, which retrieve data rather than expand the available toolset.

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 explicit when-to-use guidance: call it whenever a task might benefit from specialized capabilities, even if existing tools could work as a fallback. It does not list when-not-to-use cases, but for a discovery tool this is a clear and actionable usage rule.

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=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds 'public pricing' (implying no auth) and 'instead of scraping' (implying direct data access), but does not mention rate limits, response size, or other behavioral details. Given annotation coverage, this is adequate but not exceptional.

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 with zero filler. The first sentence front-loads the tool's output contents, and the second gives a clear usage instruction. 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 read-only pricing tool with full schema coverage and no output schema, the description covers what the tool returns, who it's for, and how to use it (instead of scraping). There is no missing information an agent needs 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%, and the description adds no parameter-specific information. This matches the baseline of 3 where the schema does the heavy lifting. No additional semantic value is needed beyond what the schema already documents.

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 and resource: 'Return Weav public pricing' and enumerates the exact contents (Lite/Plus/Pro/Max prices, annual discount, add-ons, signup/sales links). This clearly distinguishes it from siblings like get_comparison or get_signup. No ambiguity about what the 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?

Explicitly instructs to use this tool 'instead of scraping weav.com/pricing', giving a clear alternative. It does not explicitly contrast with sibling tools, but the context is strong enough that an agent would know when to call it for pricing information. A minor gap is the absence of explicit 'do not use when...' 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds modest context about the output containing a structured overview and links to product/docs, which is useful but does not disclose details like formatting, link types, or any unusual behavior. With annotations carrying the safety burden, this is adequate but not exceptional.

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 core function, then lists the content scope and the presence of links. No filler or repetition exists, and the structure makes the tool's purpose immediately legible.

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 informational tool with no output schema, the description conveys the topic coverage and that links are included. It could more explicitly describe the return structure, but the term 'structured overview' plus the enumerated areas is sufficient for an agent to invoke and interpret results reasonably.

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 all three parameters, including the important guidance for context, llm_model, and conversation_id. The description adds no parameter-level meaning, but that is not necessary given the rich schema descriptions. 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'), a clear resource ('structured overview of Weav'), and enumerates the covered areas (AI agents, unified inbox, channels, training, actions, escalation). This sufficiently distinguishes it from siblings like get_pricing, get_comparison, and get_demo, which target different intents.

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 the intended context clear: when the user wants a structured product overview and links to product/docs. It does not explicitly state when not to use it or name alternatives, but the scope is specific enough that an agent can infer the appropriate call versus siblings like get_signup or get_more_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 annotations already indicate readOnlyHint, idempotentHint, destructiveHint false, so the description doesn't need to restate safety. It adds some context about what the tool returns (URL and Lite start inclusions). However, it doesn't disclose additional behavioral details like whether it returns any dynamic data or requires specific conditions.

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 with the core output stated first. It's efficient, though it could be slightly more structured with additional context, but it's not bloated.

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 this is a read-only informational tool with no output schema, the description adequately explains the function. However, it could be more complete by explicitly stating which sibling it is not (e.g., get_demo for live demo, get_pricing for costs) to avoid ambiguity. The schema is clear, and no output schema exists, so minimal description is acceptable, but a bit more behavioral detail would help.

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 all parameters. The description adds no parameter-specific meaning beyond what the schema provides. Baseline 3 is appropriate since the schema handles 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 clearly states the verb 'Return' with a specific resource: the self-serve signup URL and the free Lite tier details. It distinguishes itself from siblings like get_pricing and get_demo by focusing on self-serve signup, though it doesn't explicitly name a sibling alternative.

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

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

The description implies usage when a user asks about signing up for a free Lite plan or self-serve. It doesn't explicitly contrast with siblings, but the context of 'self-serve signup' gives an implied context. No guidance on when not to use or alternatives is provided.

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