Refund eligibility notary for US subscriptions. Returns ALLOWED/DENIED/UNKNOWN.
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Average 4.5/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of confusion between tools. The tool's purpose is clearly stated and unique.
The single tool name 'refund_eligibility' is descriptive and follows a noun-style pattern, which is acceptable. Since there is only one tool, consistency is inherently maintained, though it does not follow a verb_noun convention.
A single tool is borderline for a service called 'refund-decide'. It could be sufficient for a narrow decision endpoint, but the server feels thin for anything beyond basic eligibility checks.
The server offers only a single evaluation tool with no supporting operations (e.g., rule inspection, manual review, or decision history). This is a significant gap for a refund decision service, as there is no way to understand or act on the UNKNOWN outcome.
refund_eligibilityCheck refund eligibilityAEvaluate a US consumer subscription refund against versioned source-backed rules. Returns UNKNOWN when required conditions are absent or the vendor requires approval or manual policy review.
| Name | Required | Description | Default |
|---|---|---|---|
| plan | Yes | Plan type. Currently only 'individual' plans are supported. | |
| region | Yes | Region code. Currently only 'US' is supported. | |
| vendor | Yes | Vendor identifier (lowercase, underscore-separated). | |
| days_since_purchase | Yes | Number of days since the subscription was purchased. | |
| qualifying_conditions_met | No | Required when the vendor policy has source-specific conditions such as first purchase, unused benefits, or an eligible annual plan. |
| Name | Required | Description |
|---|---|---|
| code | Yes | |
| vendor | No | |
| message | Yes | |
| verdict | Yes | |
| refundable | No | |
| source_hash | No | |
| window_days | No | |
| evaluated_at | No | |
| rules_version | No | |
| policy_version | No | |
| rulebook_result | No | |
| required_context | No | |
| policy_source_url | No | |
| days_since_purchase | No | |
| policy_last_checked | No | |
| policy_source_notes | No | |
| policy_last_verified_utc | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds important behavioral context beyond the annotations by disclosing that the tool returns UNKNOWN when required conditions are absent or when the vendor requires approval/manual policy review. This complements the readOnlyHint and idempotentHint annotations, providing the agent with actionable edge-case behavior that is not otherwise stated.
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, well-structured sentence that is front-loaded with the core action and includes the most important edge-case behavior. Every word contributes value, with no redundancy or unnecessary detail.
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 presence of a rich input schema, a clear output schema (indicated by 'has output schema: true'), and comprehensive annotations, the description covers the essential behavioral nuance (UNKNOWN return cases). The tool is simple enough that this description, combined with the structured data, is fully complete for an agent to select and invoke it correctly.
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 provides 100% coverage of parameter descriptions, including enums and required fields. The description itself does not add detailed parameter-level semantics beyond mentioning 'required conditions', which maps to the optional qualifying_conditions_met parameter. Since the schema already fully documents parameters, the baseline score of 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 clearly states the tool evaluates a US consumer subscription refund against versioned source-backed rules, using a specific verb ('Evaluate') and resource ('refund'). It also distinguishes the tool's scope by mentioning 'US consumer subscription' and 'versioned source-backed rules', which aligns with the title 'Check refund eligibility' and the schema constraints.
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 context on when to use the tool: for evaluating US consumer subscription refunds against source-backed rules. It does not explicitly mention exclusions or alternatives, but given there are no sibling tools and the context is well-defined, this is a strong usage guideline without needing exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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