Insurance Research Expert by Spot

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

Beyond the annotations (readOnlyHint=true, destructiveHint=false), the description reveals how the answer is constructed: every factual statement is copied from validated evidence and linked to its source. This is significant behavioral context—users know the output is bounded, sourced, and not freely generated—which adds real value over the structured 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?

Two sentences with no wasted words: the first states the purpose, the second states the behavioral guarantee. The most important information is front-loaded.

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 single obviously named parameter, the presence of an output schema, and annotations covering safety, the description provides all necessary context. It even clarifies sourcing and linking behavior, leaving no critical gap for an agent deciding how to invoke the 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 0%, so the description carries the burden of explaining the single 'question' parameter, and it does not explicitly do so. However, the parameter is self-evident from its name and the tool's 'generate an answer' purpose, and no additional formatting or scope details are strictly needed. The description provides indirect meaning but does not fully compensate for the absent schema description.

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-object pair: 'Generate a bounded answer' from 'retrieved public evidence.' This clearly distinguishes it from sibling tools like search_knowledge (searching), get_page (fetching a page), and compare_providers (comparing) without needing to open those definitions.

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 clearly communicates the intended use: produce a grounded, evidence-based answer rather than raw search results or page content. It does not explicitly name alternatives or provide when-not-to-use exclusions, but the purpose is contextually distinct enough for an agent to select it appropriately.

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 read-only and non-destructive behavior con consigo. The description adds extra behavioral context: the comparison is published and sourced, and aliases or reversed parameter order resolve to the same canonical comparison. This goes beyond what the annotations or schema state.

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 concise sentences. The core action is front-loaded, and the canonicalization/alias detail is presented efficiently with no filler or redundancy.

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 two-parameter read-only tool with an output schema, the description covers the key facts: published source, canonical comparison, and order-insensitivity. It does not explicitly route the agent away from sibling tools, but the core invocation context is sufficient.

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 input schema has no parameter descriptions (0% coverage), so the description must compensate. It implies both parameters are provider identifiers and that aliases/order are handled, but it does not define what a provider identifier looks like or clarify required input formats. Minimal but useful added meaning.

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 ('Retrieve') and a clear resource ('published, sourced provider comparison'). It clearly distinguishes this from the sibling tools by focusing on a canonical provider-to-provider comparison rather than general search, page retrieval, or questions.

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 utility is implied: use this tool when you need a direct provider comparison. However, the description does not explicitly mention alternatives such as search_knowledge or get_page, nor does it state when not to use this tool.

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 establish read-only, non-destructive, closed-world behavior. The description adds value by clarifying that only registered public pages are accessible and that drafts/guides/submissions/unknown paths will not be found. This helps set agent expectations about what kinds of paths will fail without contradicting 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?

The description is a single, well-structured sentence pair with no redundant wording. The primary action and scope are front-loaded, and the exclusion list follows naturally. Every clause 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 one-parameter read tool with an output schema and clear annotations, the description is complete. It covers the target resource, the lookup input, and the main failure categories. Return value details are already covered by the output schema, so nothing critical 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 schema provides zero description coverage for the path parameter, so the description must compensate. It adds meaning by specifying 'canonical path' and 'registered public page,' which is useful but still somewhat ambiguous—it does not give an example or format for a canonical path. The description partially compensates but leaves room for interpretation.

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 (retrieve), a resource (registered public page), and a key selection criterion (canonical path). It also lists what is excluded (drafts, guides, submissions, unknown paths), making the tool's scope unmistakable. This clearly differentiates it from the sibling tools, which involve asking, comparing, and searching.

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 provides clear context for when the tool applies: only published public pages with canonical paths. It explicitly states that drafts, guides, submissions, and unknown paths are unavailable, guiding an agent away from invalid calls. It does not name sibling alternatives, but the sibling tools are functionally distinct enough that no exclusion is necessary.

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 description adds behavioral context beyond the annotations: it states the search is deterministic and lexical, which informs the agent about result matching behavior. It also specifies 'published' content, indicating drafts or unpublished items are excluded. This goes beyond the readOnlyHint and destructiveHint annotations, adding valuable operational detail.

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, focused sentence that front-loads the action ('Search') and key object types. Every word adds value, and it is appropriately concise for a simple search tool. 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?

The tool has a simple schema with an output schema, so the description doesn't need to detail return values. It covers the key operational aspect (deterministic lexical retrieval) and scope (published content). It lacks explicit guidance on when to use this over siblings, but given the output schema and annotations, it is largely complete for a search 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 0%, so the description must compensate for missing parameter documentation. It does not explain the query parameter's expected format or semantics beyond implying it's a text query, nor does it describe the limit parameter's effect (e.g., max results). The description provides almost no guidance on how to use the parameters, leaving the agent to infer from names and constraints.

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 searches published Spot pages, provider profiles, and comparisons with deterministic lexical retrieval. It names specific content types, making the purpose clear. However, it does not explicitly differentiate from sibling tools like ask_spot or compare_providers, though the mention of 'deterministic lexical retrieval' hints at a keyword-based search as opposed to semantic Q&A.

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 provides some usage context by specifying it searches published content with deterministic lexical retrieval, implying it's for exact-keyword searches rather than natural language queries. However, it does not explicitly state when to use this tool versus alternatives like ask_spot (likely Q&A) or compare_providers (comparison). No exclusions or conditions are given.

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