openemr-mcp

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While 'longitudinal' hints at time-series data, the description lacks critical safety context: mutation vs. read-only status, PHI handling considerations, error behavior when labs are missing, or data freshness. For a healthcare tool, this is a significant gap.

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, efficient sentence that front-loads the action verb. Every element serves a purpose: the verb establishes the operation, 'longitudinal' implies the data structure, and the parenthetical examples define the scope. No words are wasted.

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 100% schema coverage for three straightforward parameters and no output schema, the description adequately covers the input contract. However, without annotations or output schema, it omits important contextual details about the return format (e.g., JSON structure, timestamp formatting) and clinical safety considerations expected in healthcare tooling.

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 has 100% description coverage, establishing a baseline of 3. The description parenthetically lists the specific lab metrics (A1c, LDL, eGFR), which reinforces the enum values in the 'metrics' parameter, but does not add syntax details, format constraints, or clinical definitions beyond what the schema already provides.

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 ('Return') with a clear resource ('longitudinal lab trajectories') and explicitly scopes the tool to specific lab metrics (A1c, LDL, eGFR). This effectively distinguishes it from siblings like openemr_vital_trends and openemr_health_trajectory by specifying the exact clinical domain.

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 context by specifying it handles lab data (vs. vitals or questionnaires), but provides no explicit when-to-use guidance, prerequisites (e.g., patient consent), or comparisons to alternatives like openemr_health_trajectory. The guidance remains implicit rather than directive.

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