openemr-mcp

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 computes 'clinical drift alerts', indicating analytical processing beyond simple retrieval. However, it omits whether this is read-only, if alerts are persisted to the EMR, performance characteristics for large windows, or what constitutes a 'drift'.

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, efficiently structured sentence that front-loads the primary action ('Aggregate'). Every clause earns its place: the parenthetical metric enumeration clarifies scope, and 'clinical drift alerts' defines the unique output. No redundancy or waste.

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 absence of an output schema, the description adequately signals the return value nature ('clinical drift alerts'). For a tool with 100% input schema coverage and moderate complexity, this is sufficient, though it could be improved by noting the return format or alert severity levels.

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?

While the schema has 100% coverage providing baseline documentation, the description adds semantic value by enumerating the specific metric categories ('labs, vitals, questionnaires') that the 'metrics' array parameter can subset, effectively documenting the domain of valid values beyond the generic schema description.

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 specific verbs ('Aggregate', 'compute') and resources ('metric trajectories', 'clinical drift alerts'). It explicitly enumerates the metric types ('labs, vitals, questionnaires'), effectively distinguishing this from sibling tools like openemr_lab_trends, openemr_vital_trends, and openemr_questionnaire_trends by emphasizing the holistic 'all' aggregation and unique drift alert computation.

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 for comprehensive analysis by mentioning 'all metric trajectories' and 'clinical drift alerts', contrasting with individual metric siblings. However, it lacks explicit when-to-use guidance (e.g., 'Use this instead of individual trend tools when monitoring for holistic deterioration') or prerequisites.

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