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get_glucose_history

Read-only

Retrieve historical glucose readings for analysis, review past levels, identify patterns, or check overnight values. Default returns 24 hours of timestamped data.

Instructions

Retrieve historical glucose readings for analysis. Returns an array of timestamped glucose values. Useful for reviewing past glucose levels, identifying patterns, or checking overnight values. Default retrieves 24 hours of data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hoursNoNumber of hours of history to retrieve (1-168). Default: 24. Examples: 1 for last hour, 8 for overnight, 168 for one week. Note: LibreLinkUp only stores approximately 12 hours of detailed data.
Behavior4/5

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

Annotations already declare readOnlyHint=true, indicating a safe read operation. The description adds valuable behavioral context beyond annotations: it specifies the default time range ('Default retrieves 24 hours of data') and hints at data limitations ('Useful for... checking overnight values'), which helps the agent understand usage constraints without contradicting annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and return format, followed by usage examples and default behavior. Every sentence adds value: the first states the action and output, the second gives use cases, and the third specifies the default. There is no redundant or wasted text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (one optional parameter) and annotations covering safety, the description is largely complete. It explains the purpose, usage, and default behavior. However, without an output schema, it could benefit from more detail on the return structure (e.g., format of timestamped values), though the mention of 'array of timestamped glucose values' provides basic context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents the 'hours' parameter. The description adds marginal value by implying time-based retrieval ('Default retrieves 24 hours of data') but does not provide additional syntax or format details beyond what the schema already covers. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Retrieve historical glucose readings') and resource ('glucose readings'), distinguishing it from siblings like get_current_glucose (current readings) or get_glucose_stats (statistical summaries). It explicitly mentions the return format ('array of timestamped glucose values'), which helps differentiate its purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool ('for reviewing past glucose levels, identifying patterns, or checking overnight values'), but it does not explicitly state when not to use it or name alternatives (e.g., get_glucose_trends for trend analysis vs. raw data). This gives good guidance but lacks explicit exclusions or sibling comparisons.

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

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