Medicare Enrollment and Revalidation Data

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, but the description adds substantial behavioral specifics: it lists exactly what will be returned (matching enrollments, due date or TBD, source metadata, submission-status limits, monitoring and automation handoffs), and explicitly states limitations ('free read-only tool cannot open PECOS, submit a revalidation, start a paid run, open checkout, or purchase anything'). This exceeds what annotations provide and gives rich transparency.

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 exactly two sentences, front-loaded with the core purpose. Every clause adds meaning—limits, return contents, negative boundaries—and there is no filler or duplication of schema/annotation data.

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?

For a single-parameter, read-only checklist tool, the description is complete. It explains the input limit (100 NPIs), the output structure (returns matching enrollments, due date/TBD, source metadata, submission-status limits, handoffs), and limitations. Since there is no output schema, the description compensates by detailing return contents.

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 schema already covers 100% of the parameter meaning with a detailed description of the 'npis' array, including constraints and invalid NPI handling. The tool description adds context about the CMS list but does not materially enhance the schema's parameter semantics. Baseline 3 is appropriate.

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 opens with a specific verb and resource: 'Check up to 100 NPIs against the current public CMS Medicare Revalidation List.' This clearly states the tool's function and scope, distinguishing it from sibling tools like 'get_medicare_revalidation_automation_offer' or 'get_pending_medicare_behavioral_health_preview', which are about offers/previews rather than the actual check.

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 conveys the primary use case (checking NPIs against the CMS list) and what the tool cannot do (open PECOS, submit revalidation, purchase). However, it does not explicitly mention when to prefer this tool over its siblings or provide exclusions. The context is clear, but without explicit comparisons, it falls just short of a 5.

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

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

Annotations already declare read-only, idempotent, open-world, and non-destructive hints. The description adds meaningful behavioral context beyond this: it cannot authenticate, create tasks, start runs, or purchase, and it requires explicit user confirmation before any separate paid action. This transparently sets expectations for agent behavior.

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 two sentences with dense, front-loaded information. The first sentence enumerates the exact deliverables, and the second sentence provides the behavioral boundary. No words are wasted, and all content contributes to understanding.

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?

For a parameter-free, read-only informational tool with no output schema, the description is complete. It specifies the exact return contents, clarifies its non-executable nature, and provides actionable guidance for the agent. No critical information is missing.

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 tool has zero parameters, giving a baseline of 4. The description correctly focuses on output content rather than parameter details, and no parameter documentation is needed because the schema is empty and 100% covered by the absence of 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 uses a specific verb ('Return') and explicitly enumerates the exact resource and contents: $0.01-per-returned-NPI price, 100-NPI limit, Apify Store and MCP paths, buyer-paid condition, templates, and fulfillment expectation. This clearly distinguishes it from sibling tools focused on due dates, roster watch, or behavioral health previews.

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 clearly states when to use the tool ('informational read-only') and what it cannot do (authenticate, create task, start run, purchase), and it directs the agent to require user confirmation before separate paid actions. However, it does not explicitly name alternative sibling tools for those paid/actionable operations, so it falls short of full alternative guidance.

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

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

Annotations already declare readOnlyHint and destructiveHint, but the description adds crucial behavioral context: the tool cannot open checkout, subscribe, or purchase, and it must precede explicit user confirmation. It also discloses automatic-fulfillment expectations and public-source limitations, which go beyond the structural hints and prevent misuse.

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 two sentences, front-loaded with the concrete return payload, and every clause adds value—detailing scope, limitations, and required user confirmation. There is no redundancy or filler.

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?

For an informational read-only tool with no parameters and no output schema, the description fully covers the return content, boundaries, and expected agent behavior. It leaves no ambiguity about what this tool does or how it should be used in a workflow.

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 tool has zero parameters, and the schema is fully covered by the description with no properties. Per the rubric, a baseline of 4 is appropriate when there are no parameters and the description does not need to compensate for missing parameter information.

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') and resource ('Medicare Roster Watch offer') and enumerates exactly what is provided: monthly price, NPI scope, delivery, limitations, and paths. It clearly distinguishes this from sibling tools by naming the specific offer and explicitly stating it is informational and read-only.

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 states when to use the tool: to present the offer and require explicit user confirmation before any separate checkout or purchase. It excludes actions like opening checkout, subscribing, or purchasing, which serves as a clear usage boundary. It does not name alternative tools, but the context is sufficient for an agent to decide.

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

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

The annotations already mark readOnlyHint and destructiveHint, and the description reinforces these with concrete prohibited actions (start Apify run, checkout, contact applicants, purchase). It also adds useful context about validation, row count, source dates, and limitations beyond what annotations convey.

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 two sentences, front-loaded with the main action and expected result. Every phrase adds specific information without filler or redundancy.

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?

Despite no output schema, the description outlines the return contents (source dates, national counts, limitations, optional handoff) and the data scope. The simple optional parameter and strong annotations make the description sufficiently complete for a read-only preview 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?

The only parameter, states, is fully documented in the input schema with description, pattern, and maxItems. The tool description does not add further semantic detail about how filtering works, so the baseline 3 for high schema coverage is appropriate.

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 the specific verb 'Return' and identifies the exact resource: a current validated 10-row public preview of behavioral-health NPIs newly present in CMS pending enrollment files. It clearly distinguishes from sibling tools by focusing on this preview data rather than revalidation dates or automation offers.

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?

It provides clear context for when to use the tool (to get the public preview) and explicitly states what it cannot do (start an Apify run, open checkout, contact applicants, or purchase anything). It stops short of naming alternative sibling tools, so it does not receive a 5.

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