GoAI Moat Brand Intel Memory

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

With no annotations provided, the description carries the full burden of behavioral disclosure. The verb 'List' inherently implies a read-only operation, and 'all remembered brands' indicates the scope. However, it does not disclose potential pagination, ordering, side effects, or authorization requirements, which a fully transparent description might include.

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, efficient sentence with no filler. It front-loads the verb and resource, then states the optional filter. Every word contributes to the meaning.

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 is simple, with one optional parameter and an output schema available, so the description does not need to explain return values. However, it lacks any mention of valid category values or differentiation from sibling tools, leaving the agent to guess context for list_brands versus recall or remember.

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's mention of the optional category filter adds no information beyond the schema's 'Optional category filter.' The parameter's meaning is already fully documented, so the description does not compensate for any gap or add new semantic nuance.

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 a specific verb ('List') and resource ('all remembered brands'), and mentions the optional category filter. It does not explicitly differentiate from siblings 'recall' and 'remember', but 'list' plus 'brands' is unambiguous enough for basic selection.

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 provided about when to use this tool versus the sibling tools 'recall' or 'remember'. The description only states what the tool does, leaving the agent to infer usage context from names alone.

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 itself must carry behavioral weight. 'Retrieve' reasonably signals a non-mutating read operation, which is useful. However, it does not describe edge-case behavior such as what happens when no facts exist, whether all facts are returned or only the latest, or how brand matching works. The output schema may cover some of this, but the description adds little beyond the verb.

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 one short, front-loaded sentence with no filler or redundancy. Every word contributes to the core meaning of the tool.

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 tool with a complete input schema and an output schema present, the description is largely sufficient. The only meaningful gap is the lack of explicit usage guidance relative to sibling tools, but the core call pattern is clear.

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 single parameter brand_name is already well documented. The tool description adds no additional meaning about the parameter beyond what the schema states, 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 uses a specific verb ('Retrieve') plus a clear resource ('previously stored facts about a brand'). This distinguishes it from the sibling tools: list_brands deals with brand entities, and remember implies storing new facts, whereas this tool retrieves existing fact data.

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 phrase 'previously stored' implies this tool is for recalling existing facts rather than creating or updating them, which gives some usage context. However, it never explicitly names alternatives like remember or list_brands, and it does not state when the agent should NOT 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?

With no annotations, the description must carry full behavioral disclosure. It says 'store' (a write operation) but does not explain side effects like overwriting, duplication, or idempotency. It also does not mention authentication or return behavior. The phrase 'for later recall' hints at persistence, but key operational details are missing.

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, efficient sentence that front-loads the core action and purpose. It contains no fluff, but it is slightly terse—it could include a hint about usage without becoming verbose. Still, it earns a 4 for clarity and economy.

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 is simple and the schema is fully documented, so the description is adequate for basic invocation. However, it lacks guidance on when to use this tool versus recall, and behavioral transparency is thin. Given an output schema is present, return value details are likely covered, so the main gaps are usage and side-effect disclosure, making this a 3.

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%, so each parameter has a clear description. The tool description adds little beyond the schema—it restates the brand/facts relationship but does not clarify the category parameter or any formatting details. Baseline 3 is appropriate given high 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 verb 'store' and the resource 'key facts about a brand', with the explicit purpose 'for later recall'. This distinguishes it from siblings: list_brands (listing) and recall (retrieving). It is specific and non-tautological.

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—you store facts when you want to recall them later—but it does not explicitly contrast with the sibling tools or provide when/when-not conditions. The agent must infer that this is for saving data and recall is for retrieving it, which is a mild gap.

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