Calculate expected and risk-adjusted profit for an opportunity.
| Name | Required | Description | Default |
|---|---|---|---|
| reward_cents | No | ||
| opportunity_id | No | ||
| platform_fees_cents | No | ||
| success_probability | No | ||
| execution_cost_cents | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already communicate that this is a read-only, non-destructive operation, so the description does not need to repeat safety behavior. It adds a mild behavioral trait by framing the calculation as 'expected and risk-adjusted', implying use of success_probability and costs, but it stops short of describing formula assumptions, edge cases, or output effects.
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 one direct sentence with no filler, and the key verbs ('Calculate expected and risk-adjusted profit') appear first. It earns every word and is easy to scan.
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?
There is no output schema, yet the description does not explain what the tool returns or in what form. Given five unmodeled parameters and no usage context, an agent has inadequate information to invoke it confidently or interpret the result.
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?
With 0% schema description coverage and 5 parameters, the description needed to explain how the inputs relate, but it only refers generically to 'an opportunity'. The parameter names are self-descriptive (reward_cents, success_probability), yet the description does not map them into the profit formula or clarify that all parameters are optional despite the schema marking none as 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 names a specific action ('Calculate') and a clear resource/outcome ('profit for an opportunity'), and adds the useful nuance of expected vs risk-adjusted profit. It is unambiguous on its own, though it does not explicitly differentiate from close siblings such as plan_profitable_execution or rank_profitable_opportunities.
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?
No usage context is given: the description does not state when to choose this over sibling tools, what inputs should be present before calling, or any alternative tool names. The closest hint is the word 'opportunity', but there is no when-to-use or when-not-to-use 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.
There are multiple clusters of near-duplicate tools: earn, earn_now, earn_loop, find_money_opportunities, search_global_earn, and several rank_real_profit_opportunities variants. Even with descriptions, an agent would struggle to choose reliably among dozens of overlapping search, earn, and ranking entry points.
Most tools follow a snake_case verb_noun pattern, which provides some consistency. However, the set mixes prefixes like agentlot_, standalone verbs like earn and me, and many semantically interchangeable verbs such as find, search, discover, rank, route, and list applied to similar objects.
With 124 tools, this is an extreme mismatch for a coherent server surface. Even for a broad marketplace, this many entry points creates severe navigation overhead and includes multiple generations of similar tools instead of a disciplined, minimal API.
The tool set broadly covers marketplace lifecycles: listings, requests, orders, delivery, disputes, payouts, projects, and assets. However, there are notable gaps such as updating/unpublishing listings, canceling/refunding orders, and other core lifecycle management operations that would be expected in a complete marketplace surface.