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

No annotations are provided, so the description bears full responsibility for disclosing behavior. It reveals that data is broken into four time blocks, but does not explain whether the tool fetches data from Dexcom automatically, how the `data` parameter interacts with `minutes`, or what side effects or return format are expected. The phrase 'persistence layer integration' is opaque.

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 compact and front-loaded: a purpose sentence, a clear list of time blocks, and a concise Args section. Every sentence contributes value, although 'find when problems happen' is slightly redundant with the first clause. The structure is easy to scan.

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 and no annotations, the description should explain what the tool returns and how it behaves. It does not mention the return format, nor does it clarify the relationship between `minutes` and `data`. However, it covers the core purpose and parameters adequately, making it minimally viable with clear gaps.

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?

Since schema description coverage is 0%, the description compensates by explaining all four parameters: minutes (range 1-1440, default 1440), low/high thresholds (defaults 70/180 mg/dL), and data (optional external readings). This adds meaningful units and intent beyond the raw schema, though 'persistence layer integration' remains somewhat unclear.

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: 'Analyze glucose by time of day - find when problems happen.' It further specifies time-block breakdowns (overnight, morning, afternoon, evening), which distinguishes it from sibling tools like get_statistics or detect_episodes by focusing on temporal patterns.

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 time-of-day focus implies when to use this tool, but there is no explicit guidance on when to choose it over alternatives like get_glucose_readings or get_statistics, nor any exclusions. The description provides context but leaves the decision to the agent's inference.

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 full responsibility for behavioral disclosure. It states that it checks thresholds but does not describe the output format (e.g., whether it returns a list of triggered alerts, a boolean, or a severity level), any side effects, or what 'current' means (e.g., latest reading vs. real-time stream). This is minimal for a tool that must be invoked correctly.

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 sentences plus an Args list, all front-loaded. The first sentence states the action, the second adds the 'real-time alerting' context, and the Args section is concise and directly tied to the schema. No irrelevant information, 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?

The tool is simple, but the description does not explain the return value or how to interpret the result. Since there is no output schema, the agent is left guessing what the tool returns. It covers the parameter semantics well but misses the output behavior, which is a significant gap for a tool with no annotations.

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 only provides titles and defaults, with 0% description coverage. The description compensates by listing each parameter with its semantic role (urgent_low, low, high, urgent_high) and default value, giving an agent concrete meaning beyond the schema. It doesn't explain how thresholds are applied, but the meaning of each parameter is clear.

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 'Check current glucose against alert thresholds,' a specific verb+resource pairing that clearly distinguishes this tool from sibling tools like get_current_glucose (which only retrieves) and get_statistics (which aggregates). The purpose is unmistakable and actionable.

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 'Simple threshold check for real-time alerting' implies a usage context but does not explicitly state when to use this tool over alternatives or exclude other cases. For example, it doesn't mention that detect_episodes might be more appropriate for historical episodes or that this is only for current readings.

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 explains time window mechanics and the optional data parameter for persistence layer integration, but does not disclose the return format or explicitly state that it is a read-only operation. While not contradicting any annotations, the description provides only partial behavioral context.

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 well-structured: a one-line purpose, a clearly labeled Args section, and concise examples. Every sentence adds value, with no redundant or filler text.

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?

Covers all parameters and provides usage examples, but lacks an explicit description of the return value. Since there is no output schema, the description should state what the tool returns (e.g., list of episodes). Additional guidance on when to use Dexcom API versus provided data would improve completeness, but the description is mostly thorough.

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%, but the description thoroughly documents all six parameters, including defaults, ranges, and the schema for the data parameter. The examples demonstrate parameter combinations clearly, fully compensating for the lack of schema descriptions.

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 'Detect hypoglycemic and hyperglycemic episodes' with a specific verb and resource. It is distinct from siblings like get_episode_details, which retrieves details about episodes. The examples further reinforce the purpose.

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?

Examples show common usage patterns (e.g., minutes=180, start_minutes/end_minutes), implying when to use the tool, but there is no explicit comparison to alternative tools or exclusions. The description does not state when to use this tool versus get_episode_details or check_alerts.

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?

Without annotations, the description carries the behavioral transparency burden. It discloses the return data structure ('clean, consistent data structure'), the optional data input behavior ('data: Optional external readings to format/export instead of fetching'), and detailed time-window semantics in the Args section. It does not mention side effects, authentication requirements, or error conditions, but the tool appears to be a read-like export with no destructive behavior.

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 front-loaded with a two-sentence summary, then a compact Args block with one-line parameter explanations, and ends with two concrete examples. No word is wasted, and the structure is 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?

The description covers the tool's purpose, usage pattern, all five parameters with defaults, and provides examples. It is missing explicit conflict-resolution behavior (e.g., when both minutes and start/end_minutes are provided) and a precise output format specification, but given the absence of an output schema, it does a good job of informing the agent.

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 must fully explain parameters. It does so comprehensively: 'minutes: Time period to export (1-1440, default 1440 = 24h)', 'start_minutes...', 'end_minutes...', 'format...', 'data...'—and includes examples showing both minutes and start/end_minutes usage.

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 'Export glucose readings for persistence layer integration,' using a specific verb and resource. It further clarifies the export's purpose by stating it 'Returns clean, consistent data structure for storage in external databases' and 'Call periodically to build long-term data history,' distinguishing it from sibling read-focused 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?

It provides clear context for use ('Call periodically to build long-term data history') and describes a distinct use case for persistence integration. However, it does not explicitly name alternatives or state when not to use this tool, so it stops short of full exclusion 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 must disclose behavioral traits. It mentions Dexcom API and persistence layer integration but does not clarify safety (e.g., read-only status), side effects, output format, or failure modes. This is a significant gap for a tool with no annotation support.

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 main purpose, followed by useful clinical context and parameter explanations. Every sentence contributes, though the Args section could be tightened.

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 annotations and no output schema, the description fails to explain important aspects like what the report return value looks like, how to handle data source selection, and any prerequisites or side effects. It covers the core purpose but lacks necessary operational details 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?

Schema descriptions are absent (0% coverage), so the Args section adds meaningful semantics: minutes has range and default, and data is described as optional external readings. However, 'persistence layer integration' is vague and could be clearer.

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 generates an Ambulatory Glucose Profile (AGP) report, a specific clinical standard. It explains what the report shows (glucose percentiles by time of day) and is easily distinguished from sibling tools that fetch raw glucose readings or statistics.

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 clinical context ('used by endocrinologists') implies when to use this tool, but there is no explicit comparison to alternatives or statements about when not to use it. Sibling tools like get_statistics or get_glucose_readings are not mentioned.

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 burden of disclosure. It explains that the tool returns the most recent glucose value, trend direction, and timestamp, and imposes a 10-minute freshness constraint. However, it does not specify what happens if no reading is within the last 10 minutes (e.g., error, empty result).

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, front-loaded with the primary action. It avoids redundancy and every sentence adds meaningful information about what the tool does and its constraints.

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 parameterless tool with no output schema, the description covers the essential return fields and a critical time constraint. It could be more complete by addressing edge cases, but given the simplicity, it is sufficiently detailed.

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 schema coverage is 100% (vacuous). The description adds no parameter info because none are needed; per guidance, the baseline for 0 params is 4.

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 ('Get the current glucose reading') and identifies the resource. It distinguishes this from sibling tools like get_glucose_readings by focusing on 'current' and emphasizing recency.

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: use this tool when you need the most recent glucose reading, and it must be within the last 10 minutes. It does not explicitly name alternatives or state when not to use it, but the recency constraint effectively communicates its scope.

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 must fully disclose behavior. It describes the kind of information returned but does not mention any side effects, error conditions, or data source behavior. It mentions 'if using Dexcom API' without explaining the alternative, and does not clarify what the 'data' parameter does beyond 'persistence layer integration.'

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 entire description is one sentence plus a compact Args list. It is front-loaded with the core purpose and contains no redundant 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?

The tool has 4 parameters and no output schema, requiring the description to compensate. It provides a high-level summary of the returned context but does not describe the response structure, error handling, or the behavior of the data parameter in depth.

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 0% property description coverage, but the description adds definitions for all four parameters: minutes as a time period with range and default, low/high as thresholds with defaults, and data as optional external readings. However, the explanations are terse and leave ambiguity about the integration.

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 ('Get') with a clear resource ('detailed context for each glucose episode') and enumerates the aspects (what led to it, severity, recovery). This distinguishes it from siblings like detect_episodes, which likely identifies episodes rather than detailing them.

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 retrieving episode details but does not explicitly state when to prefer this over sibling tools like detect_episodes or get_glucose_readings. No exclusions or alternative guidance is provided.

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 window filtering behavior ('we fetch from start_minutes back and filter to the window') and the constraint start_minutes > end_minutes. With no annotations, this carries the full burden.

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?

Structured with Args, Examples, Note; each sentence adds value. The organization is clear and front-loaded with the purpose.

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 no output schema, the description covers input behavior well, but omits return value format. Still sufficient for selection and 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?

Every parameter gets meaning: minutes range, start/end semantics, max_count limit, and data's purpose. The schema has zero descriptions, so this fully compensates.

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 'Get historical glucose readings,' a specific verb+resource that clearly distinguishes this tool from siblings like get_current_glucose and get_statistics.

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 examples provide usage contexts (e.g., last hour vs time windows), but there is no explicit when-to-use vs alternatives. The historical vs current distinction is implicit.

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 burden of disclosure. It explains window semantics (e.g., minutes ago) and includes defaults, but it does not explicitly state that the operation is read-only, what statistics are computed, or how low/high thresholds affect output. The behavior is partially transparent 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?

The description is well-organized with an Args section and examples. It is concise, with no filler, and each sentence serves a purpose—whether defining a parameter or demonstrating usage. The Examples section adds practical value without bloat.

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?

Despite 6 parameters and no output schema, the description doesn't explain what the returned statistics include (e.g., average, time-in-range, hypoglycemia counts) or how low/high thresholds influence the computation. This gap leaves an agent unsure whether the tool meets its exact needs. However, the examples and parameter details provide adequate basic context.

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%, and the description fully compensates by defining each parameter's range (1-1440 for minutes), units (mg/dL for thresholds), and semantic meaning (e.g., start_minutes as 'minutes ago from now'). Examples illustrate usage clearly. The only minor gap is the vague 'data' parameter, but overall parameter semantics are strong.

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 'Get glucose statistics for a time period', specifying a specific verb and resource. This distinguishes it from sibling tools like get_current_glucose and get_glucose_readings by focusing on aggregated statistics rather than individual or current readings.

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 through parameter explanations (minutes, start_minutes, end_minutes) and examples showing different time windows. However, it does not explicitly mention when to prefer this over sibling tools or provide exclusions like 'use get_glucose_readings for raw data', so it falls short of a 5.

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 discloses the tool's return structure (current, period_stats, alerts, summary) and the meaning of each part. It also explains the minutes parameter's range and default. It doesn't mention side effects, but this is a read-only operation, and the description clearly implies no modifications. It could add notes on edge cases or data freshness, but the provided detail is solid.

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 well-organized with clear sections for Args and Returns. Each sentence serves a purpose, and the 'how am I doing?' metaphor adds clarity without verbosity. It is front-loaded with the core purpose and uses structured lists to convey complex information efficiently.

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?

Despite having no output schema, the description comprehensively explains all return fields and their content, including a plain-English summary. It also covers the parameter's range and default. For a composite summary tool, this is fully complete for an agent to invoke and interpret results. The only minor gap is that it doesn't clarify whether alerts are only for the specified period, but this is implied.

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, and it does thoroughly. The Args section explains the semantic meaning of 'minutes' as 'Time period for context stats' with a range and default, far beyond the schema's minimal definition. This gives the agent a clear understanding of how the parameter affects the output.

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 function: 'Get a complete status summary' with a specific verb and resource. It distinguishes itself from siblings by combining current glucose, trend, stats, alerts, and a plain-English summary, making it the 'how am I doing?' tool. This clearly separates it from more focused tools like get_current_glucose or get_statistics.

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 clear usage context: 'Perfect for quick check-ins, clinical dashboards, or health intelligence apps.' This implies when to use it (when an overall snapshot is needed) but doesn't explicitly mention when not to use it or name alternative tools. However, the context is specific enough to guide selection; a small deduction for missing explicit exclusions.

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