Check this account's accumulated earnings from book sales (as author) and annotation rewards. Revenue model: 70% to author, 20% to top-upvoted annotators, 10% to platform. The 20% annotator pool is only distributed when annotations have upvotes > 0.
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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?
No annotations are present, so the description carries full responsibility for behavioral disclosure. It goes beyond a simple 'check' by detailing the revenue split (70/20/10) and the condition that annotator rewards are distributed only when upvotes > 0. This adds non-obvious context, though it does not explicitly state whether it is a read-only operation or what the returned data includes.
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 of minimal length: the first states the purpose, and the second clarifies the revenue model and distribution condition. Every sentence earns its place, with no repetition 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 a zero-parameter, no-output-schema tool, the description covers the core purpose and key behavioral nuance (revenue distribution). It could be more explicit about whether the figure is gross, net, or pending, but given the simplicity, this is a minor gap and the description is largely 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?
This tool has zero parameters, so the schema is trivially complete and there is no parameter information to supply. According to the rubric, a baseline of 4 is appropriate, and the description adds no unnecessary parameter details.
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 'Check' with a clear resource: accumulated earnings from book sales and annotation rewards. It also differentiates from the sibling tool 'annotation_earnings' by explicitly including book sales, making it distinct.
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 communicates that this tool is for checking a broad earnings summary (book sales + annotation rewards), which implies when to use it. However, it does not explicitly contrast with alternatives like 'annotation_earnings' or 'author_dashboard', so there is no direct when-not guidance.
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.