Get the manual-scan logbook for a connection (~14 days of spot readings and notes). Read-only CGM data — not for medical decisions without clinician review.
Not the continuous graph — use librelink_get_glucose_graph for minute-by-minute data.
Bulk support: accepts patient_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| account | No | ||
| patient_id | No | ||
| patient_ids | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, destructiveHint, and idempotentHint. The description adds valuable behavioral context: it mentions read-only nature, cautions against medical decisions without clinician review, and highlights batch support via patient_ids. 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?
The description is concise, three sentences, with each sentence adding distinct value: purpose, differentiation from sibling, and batch support. It is well-structured 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?
Given the tool's complexity (3 params, no output schema, many siblings), the description covers the main purpose, a key distinction, and batch support. It does not explain return format or error conditions, but is fairly complete for a read-only data retrieval 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?
With 0% schema description coverage, the description must compensate. It only explains one parameter (patient_ids for batch support) and does not explain 'account' or 'patient_id' individually. This leaves significant ambiguity for two of three parameters.
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 retrieves the manual-scan logbook for a connection, specifying scope (~14 days of spot readings and notes). It distinguishes itself from the sibling librelink_get_glucose_graph by explicitly noting that tool is for minute-by-minute data.
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 usage context: it's for manual-scan logbook data, not for the continuous graph, and includes a medical disclaimer. It specifies an alternative sibling tool (librelink_get_glucose_graph). However, it could be more explicit about when not to use this tool beyond the graph comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly distinct by function, but librelink_list_accounts and librelink_list_connections are exact aliases, causing confusion. Some glucose tools have overlapping outputs (e.g., librelink_get_current_glucose vs librelink_get_latest_reading), though descriptions help differentiate them.
CGM data tools consistently use the librelink_ prefix, but authentication, connect, marketplace, report_bug, show_version, and toolkit_info lack the prefix, mixing conventions. This inconsistency makes the tool set feel disjointed.
With 16 tools, the set is slightly larger than needed, but it covers the main CGM functionalities plus platform utilities. The number is still reasonable and manageable, with each tool serving a specific purpose.
The CGM data tools provide comprehensive read-only access (current, graph, stats, logbook, etc.), but there are no write operations (e.g., setting targets) or historical data beyond ~12-14 days. The inclusion of marketplace and configuration tools feels out of scope but does not leave critical gaps for the stated domain.