Integrated AI

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context by disclosing the return payload's contents (specific prices and links), which goes beyond what the annotations provide.

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, front-loaded with the core verb and resource. It is somewhat long due to enumerating every price and link, but each clause carries real information, so waste is minimal.

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-required-parameter, read-only lookup with no output schema and full annotation coverage, the description supplies everything needed to invoke and interpret it, including the exact return contents. Nothing material 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% and only one optional parameter exists, so the schema already documents the topic parameter and its allowed values. The description's mention of Foundation/retainer/contact content loosely maps to those topic values but adds no syntax or format detail beyond the schema. 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?

States a specific verb (return) and enumerates exactly what resource content is produced: the public description, Foundation pricing, Partner retainer pricing, and three links. An agent knows precisely what this tool yields without opening the schema, and it is clearly distinct from the sibling 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?

Usage is only implied: the tool is a read-only 'about' lookup, and the topic parameter hints at selectable subtopics. The description gives no explicit when-to-use, when-not-to-use, or alternative routing, so guidance remains inferential rather than stated.

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, idempotentHint, destructiveHint=false and a closed world, so the safety profile is covered. The description adds real context beyond that: the output is a bounded set of 5-10 workflows drawn from a fixed built-in library rather than generated arbitrarily, which is meaningful with no output schema present.

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 front-loads the transformation and its output, the second enumerates the inputs. Nothing is repeated or padded.

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 carries the return-value burden and does state the count and provenance of results. Inputs are fully enumerated in the schema, and the read-only, idempotent nature is covered by annotations; the only gap is the absence of sibling routing.

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 baseline is 3. The description restates five of the six inputs in prose (line of business, team size, AI usage, data rules, bottleneck) but omits health_information and adds no format or value guidance beyond what the schema already states.

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 gives a specific verb+resource ('Take short answers ... and return 5 to 10 starting workflows') and names the source library ('built-in benefits and P&C library'), so the agent knows exactly what it produces. It does not, however, distinguish itself from the sibling find_workflows, which plausibly overlaps in 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?

Usage is implied by the input list ('asks for line of business, team size, how AI is used today...'), which tells the agent the preconditions for a useful call. There is no explicit when-to-use vs. find_workflows or build_governance_policy guidance, and no exclusions, so it stays at implied rather than stated.

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 the safety profile (readOnly, idempotent, non-destructive, closed-world). The description adds genuine behavioral context beyond them: fields are filled only where the template has a slot, the agency name is left bracketed, and it explicitly disclaims legal advice.

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?

Front-loads the action and the source template, then requirements, then caveats. The parameter enumeration partially repeats the schema, but the disclaimer and source URL are worth their space; overall it is tight and free of 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?

With no output schema, the description carries the return-value burden and does so: it says what the policy contains, how unspecified fields (agency name) are handled, and links the canonical template. An agent has everything needed to call this 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 all four parameters (state, ai_tools, agency_size, agency_type) are already documented in the schema. The description restates them at a high level ('agency type (benefits, pc, or both), state, the AI tools in use, and agency size') without adding format or edge-case 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?

States a specific verb and resource ('Draft an AI governance policy for an insurance agency') and names the source artifact ('the public Integrated AI template'). This is clearly distinguishable from siblings like assess_ai_readiness, check_data_safety, and find_workflows.

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?

Gives clear operating context: it requires agency type, state, AI tools, and agency size, and it produces a templated policy. It does not explicitly state when to prefer this over a sibling or any exclusions, but the siblings are far enough apart that routing is unambiguous.

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 cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description still adds real behavioral context beyond them: it enumerates the three return classifications plus the matched rule (valuable since there is no output schema), and discloses that the input description is not stored and that output is guidance rather than legal advice.

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?

Four sentences, front-loaded with purpose and return semantics; each sentence carries distinct information (classification meaning, matched rule, legal disclaimer, storage disclosure). It is slightly dense but nothing is redundant 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?

With no output schema, the description correctly supplies the return-value semantics, and it also covers the privacy question an agent would have about input handling. Input constraints are handled by the schema and safety hints by annotations, so the definition is nearly complete for a single-parameter read 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 coverage is 100% and the schema itself already explains the single 'description' parameter and warns against pasting SSNs, diagnoses, account numbers, or passwords. The description's 'what the task is, or what kind of data is involved' essentially restates the schema, so it adds little beyond 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?

States a specific verb (classify) and resource (a task or data description) against a named ruleset (green/yellow/red for PII and PHI). The three output categories make the tool's function unmistakable and clearly separate it from siblings like assess_ai_readiness or build_governance_policy.

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 it by telling the agent to pass a task description or data description, and the red/yellow/green semantics imply the pre-flight use case. However, it names no alternatives or explicit when-not-to-use conditions against the sibling tools, leaving routing to inference.

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, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds value beyond that by clarifying the library is 'built-in' (consistent with the closed-world annotation) and by disclosing the shape of results: matching workflows with steps and a starter prompt.

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 verb and resource, followed by the return shape. Every clause carries information; nothing is padded or repeated.

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 work of explaining what comes back (workflows with steps and a starter prompt), which is the key gap an agent would otherwise face. It does not elaborate on matching/ranking behavior or how narrowing via line_of_business interacts with query, but the essentials for correct invocation are present.

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 all three parameters (query, line_of_business, limit) are already documented, including the enum-like values for line_of_business and the 1-5 range for limit. The description's example search terms duplicate what the query parameter description already says, adding no new semantic detail. 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?

States a specific verb ('Search') and resource ('built-in library of benefits and P&C workflows') and enumerates concrete workflow types (renewal comparison, census cleanup, SBC comparison, loss-run summary, quote comparison, COI review). An agent immediately knows what the tool does and what domain it covers; the unrelated siblings (governance/readiness tools) require no further differentiation.

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 implied by the description — reach for this when you need a workflow from the built-in library — but there is no explicit when/when-not guidance and no named alternative to route against. Since the sibling tools are unrelated in function, the omission is less damaging, but it remains unstated.

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