Break down this account's annotation-pool earnings per annotation: which annotations earned how much, how much is already paid vs still pending, and the annotation's current score. If connect_required is true, you have pending earnings but must finish Stripe Connect onboarding (connect_onboard) before they can be paid out.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 provided, the description carries the full burden. It discloses what the tool returns (per-annotation amounts, paid vs pending, score) and the important conditional behavior about pending earnings requiring connect_onboard. This is meaningful behavioral context beyond a simple one-liner.
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, front-loaded with the key purpose, then a conditional caveat. Every word earns its place with no 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?
No output schema exists, so the description correctly outlines the return content: per-annotation earnings, paid vs pending split, and score. It also covers the connect_required state. It could mention the relationship to payout_balance or check_earnings, but for a simple read tool, this is sufficient.
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 coverage is 100% (vacuously). No parameter information is needed, so the baseline of 4 applies. The description adds no parameter details, but none are required.
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 ('Break down') and resource ('annotation-pool earnings per annotation'), clearly distinguishing it from siblings like check_earnings or payout_balance by detailing the per-annotation breakdown, paid/pending status, and score.
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?
Provides clear context on when to use the tool (to see per-annotation earnings details) and gives a conditional directive regarding connect_required and Stripe Connect onboarding. Does not explicitly name alternatives but the context is strong enough for an agent to differentiate.
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.