External-review outcomes for a medical condition: how often insurance denials involving this condition were overturned, plus the most-fought treatments for it.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Condition slug, e.g. 'obesity', 'autism', 'hepatitis-c' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds context about return data (overturn rates, most-fought treatments) but does not disclose additional behavioral traits beyond annotations.
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?
Two sentences with no wasted words. Front-loaded with key 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?
Simple tool with one parameter and good schema/annotations. Description adequately explains return value. Lacks differentiation from sibling tools but is otherwise complete.
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 covers the single parameter slug with 100% coverage. Description adds example values (obesity, autism, hepatitis-c), adding meaning beyond the schema.
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 the tool returns external-review outcomes for a medical condition, including overturn rates and most-fought treatments. It uses specific verbs and is easily distinguishable from siblings like get_treatment_outcomes.
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?
Implies use for condition outcome info but lacks explicit when-to-use or when-not-to-use guidance compared to sibling tools. No alternatives mentioned.
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 have distinct purposes (e.g., get_appeal_rights vs. get_state_external_review), but get_condition_outcomes, get_treatment_outcomes, and get_drug_playbook overlap in the domain of external-review outcomes. An agent may confuse which to use for a given query, especially for drugs that appear in both drug_playbook and treatment_outcomes.
All tool names follow a consistent verb_noun snake_case pattern: get_* for retrieval, list_* for enumeration, rank_* for rankings, and search_* for discovery. No mixed styles or inconsistent verb usage.
Nine tools is well within the ideal 3-15 range. Each tool covers a distinct aspect of the coverage rights domain, and the count feels neither excessive nor thin.
The tool surface covers the core workflow: determining appeal rights, researching outcomes by condition/treatment, accessing insurer metrics, and understanding state processes. Minor gaps exist, such as no direct tool for state comparison or bulk retrieval of all states' external review rules, but these are not critical to the main purpose.