bizinsured

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

No annotations are provided, so the description carries the full burden. It states the action (takes and returns) but does not disclose whether the operation has side effects, requires authentication, or is read-only. Given the lookup nature, this is adequate but not thorough.

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 function and usage guidance with no filler. The information is front-loaded and each sentence earns its place.

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

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

Given the absence of an output schema and annotations, the description provides essential details: what it does, what it returns, and when to use it. It could detail return structure or state handling, but the current description is sufficient for a classification 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?

Schema description coverage is 100%, so both parameters are already documented. The tool description reinforces the primary role of business_description but does not add new meaning beyond the schema, especially for the state parameter.

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's function: taking a natural language business description and returning matched class codes (NAICS, ISO GL, NCCI WC). This distinguishes it from sibling recommendation tools by positioning it as the upfront classification step.

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 says 'Always call this first' and explains why (to understand the user's business type before making recommendations). This provides clear when-to-use guidance and implies ordering against the other 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?

With no annotations, the description must disclose side effects, permissions, or output behavior. It only says 'Generates a handoff,' which reveals nothing about whether data is sent, whether the action is reversible, what the user sees, or what occurs after invocation. This is a complete lack of behavioral disclosure.

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

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

The description is only one sentence and front-loaded, but it is under-specified rather than concise. It omits essential context despite the tool having a nested object and multiple parameters, so brevity is a deficiency, not a strength.

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 complexity (5 parameters, 3 required, nested risk_profile, no output schema, no annotations), a one-sentence description is grossly insufficient. It does not explain the expected input flow, the meaning of return values, or the post-conditions of the handoff.

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 description adds no meaning to any of the 5 parameters or the nested risk_profile object. It does not explain what parameters are used for or how they affect the handoff, so the agent must guess at parameter semantics.

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

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

The description uses a specific verb ('Generates a handoff') and names the resource ('connect the user with a matched independent agent'), which clearly distinguishes it from sibling analysis tools like classify_business or get_carrier_recommendations. It is not a tautology and communicates the core function.

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

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

The description does not state when to use this tool versus alternatives. It implies a handoff after matching, but gives no explicit context, prerequisites, or exclusions. Sibling tools suggest different purposes, but no comparison is offered.

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, but it only states the output register. It does not disclose return format, behavior for unknown coverage types, the role of business_context, or any other behavioral traits.

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

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

The description is a single, compact sentence with no redundant wording. It is front-loaded and easy to parse, even though it could be more informative.

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

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

Given the absence of annotations and output schema, the description is too thin. It does not explain what a 'plain language' explanation looks like, how business_context affects the result, or what happens with invalid inputs.

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 description does not compensate. The phrase 'coverage type' loosely maps to coverage_type, but business_context is entirely unexplained, and no example values or constraints are given.

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 ('Explains') and resource ('commercial insurance coverage type') and adds the qualifier 'in plain language'. This clearly distinguishes it from sibling tools like suggest_coverages or get_carrier_recommendations.

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

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

No guidance is given about when to use this tool versus alternatives. The context is only implied by the verb 'explains', with no mention of scenarios, prerequisites, or exclusions.

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

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

With no annotations, the description must carry the full burden of behavioral disclosure. It mentions returning data but does not explicitly state read-only status, side effects, permissions, or limitations. The lack of such details is a gap for a tool with no other transparency signals.

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 wasted words. It is concise and clear, though it sacrifices depth for brevity. The structure is appropriate for a simple message, but not for a tool of this complexity.

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

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

The tool has a complex schema with 14 parameters, no annotations, and no output schema, yet the description is only one line. It fails to explain input requirements, output format, or the meaning of 'small commercial risk', making it grossly incomplete for effective use.

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 description does not compensate. It mentions none of the 14 parameters, including required ones like naics_code, state, annual_revenue, and desired_coverages. The phrase 'small commercial risk' provides vague context but no meaningful parameter semantics.

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

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

The description clearly states the tool returns ranked carrier recommendations with estimated premium ranges for a small commercial risk. This verb+resource combination distinguishes it from siblings like suggest_coverages or classify_business.

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 use this tool vs alternatives. The description does not mention use cases, prerequisites, or exclusions, leaving the agent without context for 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 full burden. It mentions ranking by importance but does not disclose how suggestions are determined, what inputs are used, what output format to expect, or any limitations. This is insufficient for a tool with no annotation support.

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 sentence that is front-loaded with the main purpose and includes a key behavioral detail (ranking). It is concise and easily readable, though it could benefit from a bit more detail without losing efficiency.

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 has 11 parameters, no output schema, and no annotations, this short description is incomplete. It lacks guidance on how to use the inputs, what the output looks like, and any behavioral caveats. More detail is needed for an agent to use it effectively.

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 0% and the description provides no information about any of the 11 parameters. It doesn't even hint that business_description and state are required or how they influence suggestions. The description adds no meaning 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 tool's function: it suggests insurance coverages needed by a business, specifically 'ranked by importance.' This distinguishes it from siblings like classify_business (classification) and get_carrier_recommendations (carrier recommendations).

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

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

The description implies usage for obtaining coverage recommendations but does not explicitly state when to use it vs alternatives or any exclusions. No context about prerequisites or overlapping tool usage is given, so it's only implied.

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