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

With no annotations, the description carries the burden of behavioral disclosure. It usefully adds that no personal data is involved, that this is general wellness information, and that it is not medical advice. However, it does not describe output format, potential limitations, or what the agent should expect when calling the 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 concise and front-loaded: the first sentence states the primary purpose, and the following sentences add brief safety and scope context. Every sentence earns its place without 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 single-parameter informational tool, the description provides enough context to invoke it correctly: axis_id maps to A-H, the output is educational wellness guidance, and there is no personal data. It could be slightly more complete by describing the exact shape of the returned guidance, but that is not a significant gap here.

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 only a bare string 'axis_id' with no enum or description, and schema coverage is 0%. The description compensates by indicating the axis_id must be one of the 8 axes (A-H), which is essential. Still, it does not specify exact accepted values, case sensitivity, or behavior when the parameter is omitted.

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 action ('Return mechanism explanation and general wellness guidance') and a specific resource scope ('one of the 8 HealthLens axes (A-H)'). It is clear and actionable, but it does not explicitly differentiate itself from sibling tools beyond the axis-specific subject matter.

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 explicit guidance is given about when to use this tool versus alternatives like hl_get_wellness_article or hl_search_knowledge. The description implies use for general axis information, but it does not state when-not-to-use or name better-suited siblings.

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 exist, so the description carries the full disclosure burden. It does add useful context that only published articles are returned, that no personal data is involved, and that the content is not medical advice. However, it does not mention behavior on missing slugs, auth or rate limits, or the exact shape of the metadata/summary, leaving the behavioral picture slightly thin.

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 main action is front-loaded in a single clear sentence, followed by two short clarifying caveats. The disclaimer is longer than strictly necessary for function calling, but it is compact and does not bury the core instruction.

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 tool with no annotations, no output schema, and only 0% schema coverage on its parameter, the description is under-specified. It names the result type ('metadata and summary') but not the fields, and it never says when to use this exact lookup instead of search_knowledge or one of the other siblings. The slug requirement also conflicts with the schema showing zero required parameters, which needs clarification.

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%, so the description needed to explain the slug parameter, but it only restates the property name ('by slug'). It does not specify slug format, case sensitivity, how an unknown slug is handled, or why the schema marks the parameter as optional even though the description implies it is required.

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 opens with a specific verb and resource ('Get metadata and summary for a published wellness article') and identifies the lookup key ('by slug'), so the core operation is unmistakable. It does not explicitly contrast itself with sibling tools like hl_get_axis_detail, so it stops short of full sibling-level 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?

There is no guidance on when to choose this tool over the listed siblings, nor any explicit exclusions or alternative tool names. The caveat that it is general wellness information and not personal advice implies a non-diagnostic use, but this is not framed as a 'use this when...' instruction.

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 carries the full behavioral burden. 'Safe by default' communicates that this is a non-destructive read operation, and the listed return contents (health status, version, tier config) set expectations. It does not discuss auth or rate limits, but for a zero-parameter health check this is sufficient.

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 sentence that front-loads the action and result, followed by a useful safety note. Every word earns its place with no 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 zero-parameter health check, the description is complete: it names the tool's purpose, the exact data returned, and its safety profile. The lack of an output schema is mitigated by listing the return categories directly.

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 is fully covered, so the baseline is 4. The description adds no parameter-level detail, but none is needed.

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 ('service health status, version, and tier config'), making the tool's function unmistakable. It is clearly distinct from the content-oriented sibling tools like hl_search_knowledge and hl_get_wellness_article.

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 implies this is the tool to call when service health information is needed. It does not explicitly name alternatives or exclusions, but the sibling tools are unrelated to health checks, so no routing ambiguity exists.

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. It adds valuable behavioral details: no personal data is involved, returns top 20 entries, and it disclaims medical advice. It does not describe output schema or error behavior, but for a low-risk read/search tool this is reasonably transparent.

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?

Three concise sentences, with the core purpose front-loaded, followed by result scope and a necessary disclaimer. Every sentence contributes meaning and there is no redundant or vague wording.

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 single-parameter search tool, the description covers purpose, data source, privacy boundary, result limit, and an important safety disclaimer. The main gaps are lack of explicit sibling routing and query-format guidance, but neither is critical given the tool's simplicity.

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?

There is only one parameter, 'query', and the schema provides 0% description coverage. The description indirectly defines the parameter as the search expression against the health knowledge base, but it does not clarify expected format, language, or whether natural-language phrasing is supported. The parameter is self-evident enough to make this acceptable, though not explicit.

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 a specific resource ('HealthLens TCM wellness knowledge base'), and adds useful context about the data source (TCM-MKG graph, classical books). It also specifies the result count (top 20), which clearly distinguishes it from sibling lookup or suggestion 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 is a general knowledge search tool and clarifies that it is not personalized medical advice, which gives some usage context. However, it never explicitly says when to prefer this tool over siblings like hl_get_wellness_article or hl_suggest_general_diet, nor does it provide exclusion criteria.

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 discloses that no personal data is used and that it provides general wellness information, not medical advice. With no annotations provided, this is useful context, but it doesn't describe the output format 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 concise and front-loaded with the core purpose, followed by the disclaimer. It earns its place, though the goal list could be seen as schema-like detail.

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 tool with no output schema, the description covers the main purpose and safety disclaimer. However, the 'preferences' parameter is completely undocumented, which is a notable gap for an agent trying to invoke the tool 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 0%, so the description must compensate. It lists the goal values (anti_aging, energy_boost, etc.) but does not explain the 'preferences' parameter at all, leaving the agent to guess its format and purpose.

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 general diet guidance for a wellness goal and lists the supported goals. It distinguishes itself from siblings by focusing on diet guidance, though it doesn't explicitly name a sibling alternative.

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 (when general diet guidance for a wellness goal is needed) and includes a disclaimer that it is not medical advice. However, it does not explicitly state when not to use it or mention alternatives like hl_suggest_general_motion for exercise 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. It discloses that the tool returns general wellness information, not medical advice, and includes a disclaimer to consult a professional. It also states 'No personal data', indicating privacy considerations. However, it does not explicitly mention read-only behavior or any potential side effects, leaving some ambiguity about mutation or side effects.

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 four sentences with no redundancy. The primary action and intensity levels are front-loaded, followed by necessary disclaimers. Every sentence adds value, and the structure is clean and efficient 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 the simple single-parameter tool and lack of output schema, the description covers the essential context: what it returns, allowed parameter values, and its non-medical nature. It could mention that it requires no other inputs, but that is implicit from the schema. The disclaimers are helpful, and the tool is simple enough that 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 has zero coverage for the 'intensity' parameter (no description or enum), so the description must compensate. It lists the allowed values (beginner/moderate/advanced) within the text, giving the agent concrete meaning. However, it does not clarify whether the parameter is optional (schema shows required=0) or what happens if omitted, leaving minor ambiguity.

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 'Return' and the resource 'general exercise prescription', specifying the intensity levels (beginner/moderate/advanced). This distinguishes it from sibling tools like hl_suggest_general_diet (diet) and hl_health_check (health status), making its purpose unambiguous.

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 exercise-related wellness queries but does not explicitly contrast with alternatives. It does not state 'use this for exercise, not diet' or provide any when-not-to-use guidance. The implicit context of 'general exercise prescription' suggests the appropriate use case, but explicit exclusions are absent.

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