health

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

No annotations are present, so the description carries full burden. It discloses the return fields (direction, confidence, dose, health goal mapping) but does not mention read-only nature, authentication, or rate limits. This is adequate but lacks depth.

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 effectively convey purpose and return value. No fluff; every sentence adds value.

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 tool has only 2 parameters with full schema coverage, no output schema, and moderate complexity, the description is mostly complete. It explains output fields but could mention pagination or the scope of biomarkers.

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 clear descriptions for 'supplement' and 'limit'. The description adds only a mention of return fields, not parameter details. Baseline 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 verb 'Find', the resource 'biomarkers affected by a given supplement', and specifies the returned data (direction, confidence, dose, health goal mapping). It distinguishes from sibling tools like 'get_supplements_for_biomarker' which do the reverse.

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 when you have a supplement name, but does not explicitly state when not to use it or provide alternatives. Sibling tools are not referenced, so the agent receives no negative 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?

With no annotations, the description fully discloses behavioral traits: partial coverage, non-exhaustiveness, internal labels, raw metadata, dose suppression for non-supplements, and legacy field use.

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?

Dense and informative, every sentence adds value. Front-loaded purpose and then specifics. Slightly wordy but efficient overall.

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?

No output schema, but description thoroughly explains return values (intervention types, graph_status, pathway_tags, dose fields) and caveats, providing complete context for a 2-parameter 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 covers both parameters (biomarker and limit) with descriptions. The description adds context about output rather than parameter details, meeting the baseline for 100% 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 tool finds mixed interventions affecting a biomarker, listing specific intervention types, and distinguishes it from sibling tools like get_supplements_for_biomarker which are narrower.

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?

Provides guidance on partial coverage and data semantics (graph_status, pathway_tags, dose handling), but does not explicitly compare with siblings or state when to prefer this over similar 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?

The description discloses the type of information returned (biomarkers, mechanism, safety) and implies read-only behavior. However, it does not mention authentication needs, rate limits, or potential side effects. With no annotations, this is adequate but not fully explicit.

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 ('Get comprehensive information') and lists the key content areas, making it efficient and clear.

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 info-retrieval tool with one parameter and no output schema, the description completely covers what the tool does and what the user can expect to receive.

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% with a single parameter 'supplement' described as 'Supplement name'. The description adds context that the parameter is used to retrieve comprehensive info, but does not provide additional semantics 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 clearly states the tool's purpose: 'Get comprehensive information about a supplement,' listing specific aspects (what it is, does, biomarkers, mechanism, safety). This distinguishes it from sibling tools like 'get_biomarkers_for_supplement' which are more focused.

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 the general info tool while siblings are more specific, but it does not explicitly state when to use this tool versus alternatives or provide any 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?

No annotations provided; description carries burden. It discloses that interactions come from clinical evidence and FDA-validated pairs, and includes counts. However, it omits behaviors like data freshness, pagination, or whether results are ordered. Lacks depth for a mutation-free 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?

Two concise, front-loaded sentences that convey purpose, scope, and output types without waste. Every phrase adds information.

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, description explains return categories adequately. Two parameters are well-covered. Lacks specifics on response format or limits, but sufficient for basic understanding.

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%, but description adds value by explaining what the return types are (synergies, antagonisms, etc.) beyond schema. Also clarifies the default for include_drug_interactions. Adds meaning beyond the schema alone.

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 it finds supplement-supplement and drug-supplement interactions, specifying counts and types (synergies, antagonisms, etc.). It distinguishes from sibling tools focused on individual supplement info, biomarkers, or conditions.

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?

Implied usage: use when needing interaction data for a supplement. No explicit guidance on when not to use or alternatives, but context from sibling names suggests other tools for different queries.

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 provided, but description fully discloses that results are partial/non-exhaustive, metadata is internal taxonomy not clinical verification, and pathway_tags are raw graph metadata. No contradictions.

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?

Description is clear but verbose with multiple caveats. Each sentence adds value, but it could be more concise by grouping related warnings.

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?

No output schema, so description must compensate. It mentions output fields (type labels, graph_status, etc.) but does not fully describe the response structure or return type completeness.

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 describes both parameters; description adds meaningful usage guidance (dose_value vs dose_mg) beyond schema. The high schema coverage (100%) raises baseline, but the extra info justifies above average.

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?

Clearly states the tool finds supplements affecting a biomarker, with specific exclusions. Distinguishes from siblings like get_biomarkers_for_supplement or get_interventions_for_biomarker.

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?

Explicitly lists excluded types and warns about metadata limitations. Provides parameter usage guidance (use dose_value/dose_unit). Lacks explicit when-not-to-use or direct alternatives.

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 provided, so the description carries full burden. It implies a read-only search by mentioning 'returns', but does not explicitly state safety, auth needs, or rate limits. Adequate for a non-destructive lookup 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?

Two sentences with no redundant information. Front-loaded with the core action and resource, then adds detail about output. Highly efficient.

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 3 parameters and no output schema, the description adequately explains input (condition) and output (recommendations with grades/dosages). Could mention the return format explicitly, but the context is sufficient for an agent to invoke 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 coverage is 100%, so the schema already describes each parameter. The description adds value by giving examples of condition values and stating that results are evidence-graded, but does not add new semantic details 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 clearly states the verb 'Find' and the resource 'supplements for a health condition or goal', and distinguishes from sibling tools like 'get_supplements_for_biomarker' by specifying health conditions as input.

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?

Provides examples of conditions ('sleep', 'anxiety', 'joint pain') and states it returns evidence-graded recommendations with dosages, which gives clear context. Lacks explicit exclusion criteria or guidance on when to use 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?

No annotations provided, so the description carries full burden. It discloses that results are ranked by ProofMark and explains what that is (third-party testing, certifications, quality metrics). It does not mention return format, but for a read-only query tool, this is adequate transparency.

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?

Single sentence, front-loads the purpose, no wasted words. Efficiently conveys the tool's function and key differentiator.

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?

Description is minimal but sufficient for a simple 2-parameter tool. However, with no output schema, it could have described the return structure (e.g., list of brand names with scores) or mentioned edge cases like empty results.

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 baseline is 3. The description adds context about ranking but does not elaborate on parameter details beyond the schema. It does not explain the supplement parameter format or the limit's role in quality ranking.

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 'Find' and the resource 'highest-quality brands for a supplement', and explains the ranking metric (ProofMark). It distinguishes from siblings like get_supplement_info or search_supplements by focusing on brand quality ranking.

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 (when wanting top brands for a supplement) but does not explicitly provide guidance on when not to use or offer alternatives among siblings. No exclusion criteria or context about alternatives.

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?

Discloses fuzzy matching behavior, but lacks details on return format, pagination, or error handling. With no annotations, description carries full burden; it's adequate but not rich.

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, no waste. Essential information front-loaded: what it does and when to use it.

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 search tool with one parameter, description covers purpose, usage context, and key behavior. Missing output details, but acceptable given low complexity.

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 covers 100% of the single parameter with a description. The description adds 'fuzzy matching' context, but doesn't significantly extend beyond schema. Baseline 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?

Description clearly states 'Search for a health condition by name with fuzzy matching', specifying verb, resource, and method. It also distinguishes from siblings by indicating it should be used before get_supplements_for_condition.

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?

Explicitly says 'Use this to resolve ambiguous condition names before calling get_supplements_for_condition', providing clear context for when to use this tool versus alternatives.

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, so full burden on description. Mentions fail-closed retail front door for one mode, but lacks details on auth needs, rate limits, or whether it mutates data.

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 concise sentences with critical information front-loaded. No wasted words.

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 2-param tool with no output schema, the description is largely complete. Could mention output format (e.g., returns supplement IDs) but not essential given sibling tools.

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%; description adds value by clarifying mode usage (retail titles require supplement_retail) and fail-closed behavior. Slight redundancy for query parameter.

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 it resolves a supplement identity and differentiates modes (identity vs supplement_retail). Could be more specific about what 'resolve' entails, but clear overall.

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?

Explicitly tells when to use supplement_retail mode (raw retail product titles) and mentions fail-closed behavior. But no guidance on when to use this vs sibling tools like get_supplement_info.

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