Malaysia Data Engine

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, so the description carries less burden here. It adds the substring-matching scope and the connection to NPRA records, but does not disclose additional behavior such as result limits, ordering, or relationship semantics.

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 that front-loads the action and resource, then specifies the search criterion. It contains 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?

The tool is simple, has one well-documented parameter, a rich annotation set, and an output schema, so the description is largely sufficient. It could be slightly stronger by relating itself to sibling search tools, but nothing critical is missing for correct invocation.

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 already documents the single parameter fully, including its case-insensitive substring semantics and length constraints. The description adds little beyond restating that the search is by manufacturer-name substring, so the high schema coverage sets the baseline at 3.

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 action: finding products and related NPRA records based on a manufacturer-name substring. It identifies the resource and the matching criterion, though it does not explicitly differentiate itself from sibling search 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?

The description makes it clear when this tool is appropriate: when searching by a manufacturer-name substring. It provides useful context for selection but does not explicitly state when not to use it or name alternative 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?

Annotations already convey read-only, open-world, idempotent, and non-destructive behavior. The description adds value by disclosing that results are deduplicated and sourced from dated pipeline delta artefacts, which are behavioral details beyond 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 compact sentence with no filler. It is slightly jargon-heavy ('delta artefacts'), but every part earns its place and the core behavior 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?

With a high-coverage schema, an output schema, and strong annotations, the description is largely sufficient. The main gap is not clarifying how 'deduplicated' applies or when the 'pipeline delta artefacts' are considered 'recent', but overall the definition enables correct invocation.

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 parameters are already well documented in the schema. The description adds no additional meaning for 'category' or 'window_days', but it does not need to since the schema already explains them.

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'), a specific resource ('changes from the dated pipeline delta artefacts'), and a distinguishing behavior ('deduplicated'). This makes the tool's core purpose understandable, though it does not explicitly contrast it with 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?

No guidance is given about when to use this tool versus sibling tools like find_by_manufacturer or search_pharma. The description implies a use case around recent changes, but there are no explicit conditions, exclusions, or alternative routes.

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, idempotent, non-destructive behavior. The description adds meaningful behavioral context beyond annotations by clarifying that exactly one record is returned and that category filtering defaults to all categories. No contradiction with 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, efficient sentence with no filler. The core lookup behavior and default category behavior are front-loaded, making it easy for an agent to parse quickly.

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 exact lookup with two well-documented parameters, an output schema, and strong annotations, the description is nearly complete. It could be slightly stronger by pointing to sibling search tools for non-exact queries, but that is a minor gap.

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%, with reg_no including an example and category fully documenting defaults and allowed values. The description reinforces the 'all categories by default' behavior but does not add much 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 states a specific verb ('Return') and resource ('one exact NPRA record'), and 'searching all categories by default' adds useful scope. It does not explicitly name sibling tools, but the word 'exact' distinguishes this lookup from search/find 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?

The description implies this tool is for exact registration-number lookups, especially when category is unknown. However, it does not explicitly tell the agent when to prefer search_pharma or find_by_manufacturer, nor does it state exclusions such as 'do not use for partial or fuzzy matches.'

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 provide a strong safety profile (read-only, idempotent, non-destructive), and the description adds useful context: the document is pipeline-produced and returned verbatim. However, it does not disclose much beyond that, such as freshness, persistence, or access considerations.

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 with no wasted words. It front-loads the key information: what is returned and how it is returned, which is all this parameterless tool needs.

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 parameters, an output schema available, and rich annotations covering safety, the description sufficiently defines the tool's role. An agent can invoke it correctly without needing additional behavioral detail.

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 tool has zero parameters and the schema coverage is 100%, so there are no parameter semantics for the description to clarify. It correctly avoids inventing unnecessary parameter details.

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 action ('Return') and resource ('pipeline-produced pharma health document') and emphasizes 'verbatim', which distinguishes it from sibling tools that search or verify records. This leaves little 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?

The description implies the tool should be used when the exact pharma health document is needed, but it does not explicitly say when to prefer it over siblings like search_pharma or verify_pharma_artifact. Usage context is present by implication, not explicit 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'public searchable fields' but does not disclose behavior such as result format, pagination, matching semantics, or what happens when no matches are found. This is a minimal disclosure for a search tool.

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 filler or repetition. It conveys the core action and scope efficiently, making it easy for an agent to parse quickly.

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 that the tool is a simple search endpoint with three well-documented parameters and no output schema, the description plus schema is minimally adequate for invocation. However, the absence of usage context, sibling differentiation, and any note about return structure leaves some gaps in the overall context an agent might need.

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 thoroughly documents all three parameters with descriptions, defaults, and constraints. The description adds little beyond the schema—it only implies that searching happens across 'public searchable fields' and within one category. With high schema coverage, the baseline of 3 is appropriate.

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 action ('Search') and the resource ('one NPRA category'), and specifies it operates on 'public searchable fields.' It is specific enough to distinguish from a generic search tool, though it does not name sibling tools for explicit 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?

The description gives no guidance about when to use this tool versus alternatives, no context about preferred scenarios, and no exclusions. Usage must be inferred from the generic phrasing 'Search one NPRA category,' which is not sufficient for scenario-based 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 already declare the tool read-only, idempotent, and non-destructive, so the bar is lower. The description adds useful scoping context ('current canonical', 'local') and specifies the exact artifact type 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 compact sentence with zero filler. The verb and object are immediately clear and 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?

For a simple, read-only verification tool with one optional parameter, full schema coverage, an output schema, and safety annotations, the description is sufficient. It identifies the target artifact, and the annotations and output schema cover behavior and return shape.

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% for the single optional boolean include_proof_steps, so the schema fully documents parameters. The description adds no parameter-level detail, but the baseline of 3 is appropriate because the schema carries the burden.

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 operation ('Verify') and a precise object ('the current canonical pharma graph's local Sigstore DSSE bundle'). It is clearly distinct from the sibling tools, which are lookup/search-oriented.

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 explicit guidance on when to use this tool versus the sibling find/search/get tools. The verification intent is implied but no conditions, exclusions, or alternatives are stated.

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