Check this account's Stripe Connect onboarding / payout-eligibility state. Returns whether payouts are enabled, whether details have been submitted, and any outstanding requirements. Use this to tell the author whether they can publish and receive money yet.
| Name | Required | Description | Default |
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Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses the return data (payouts enabled, details submitted, outstanding requirements) and implies a read-only operation via 'Check' and 'Returns'. However, it does not explicitly state that it has no side effects or mention any authorization requirements beyond 'this account'.
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 action and resource, and every phrase adds value. No fluff or repetition.
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 zero-parameter status tool, the description covers the essential return values and practical usage. It lacks an explicit statement of non-mutating behavior or a link to connect_onboard, but these are minor given the clear 'Check' semantics and sibling context.
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, so the baseline is 4. There is nothing to explain about parameters, and the description accurately reflects that it operates on the current account.
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 ('Check') and names a precise resource ('Stripe Connect onboarding / payout-eligibility state'). It clearly enumerates the returned information and distinguishes itself from siblings like connect_onboard (which initiates onboarding) and payout_balance (which likely shows balance).
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 explicitly states when to use it: 'Use this to tell the author whether they can publish and receive money yet.' This provides clear context. It does not explicitly name alternatives, but the sibling list and this sentence imply that connect_onboard would be used if onboarding is incomplete.
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.
Several tools have overlapping purposes. For example, list_my_books and my_books both list authored books, and author_dashboard is a superset; list_orders and my_orders are near-duplicates; check_earnings and payout_balance both report earnings. This creates confusion and risks misselection.
Naming is inconsistent. While many tools use verb_noun (list_annotations, get_book_details), others use my_* (my_books, my_orders), bare nouns (payout_balance), or compound verbs (connect_onboard). The lack of a uniform pattern makes the set feel disjointed.
49 tools is excessive for a server, especially with at least three sets of near-duplicates. The platform is complex, but many tools could be merged (e.g., my_books and list_my_books, list_orders and my_orders). This count will overwhelm agents and increase misselection risk.
The toolset covers the core domain well: discover, purchase, read, comment, review, annotate, publish, monetize, verify, and manage payouts. Minor gaps exist, such as no update/delete annotation, no delete review, and no single-item getter for annotations/reviews, but these are not critical to the main workflows.