earn_loopRank paid requests by fit and projected seller net after AgentLot's 10% marketplace fee and an optional user-supplied execution cost. Does not guarantee acceptance or income.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | No | ||
| limit | No | ||
| skills | No | ||
| currency | No | ||
| minimum_net_cents | No | ||
| estimated_cost_cents | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only and non-destructive. The description adds valuable behavioral context by noting that results are projected and that acceptance or income are not guaranteed, and it discloses the 10% fee assumption.
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 focused sentences with no filler. The main action is front-loaded, and the caveat about non-guaranteed outcomes is concise and useful.
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?
With six undocumented parameters, no output schema, and no usage guidance, the description is not fully complete for an agent deciding what to pass and when to use this tool. The annotations cover safety, but the operational context is thin.
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?
Schema description coverage is 0%, so the description must compensate. It clarifies estimated_cost_cents as an optional user-supplied execution cost and explains the net-after-fee logic, but it does not explain goal, limit, skills, currency, or minimum_net_cents beyond their parameter names.
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 states a specific operation: ranking paid requests by fit and projected seller net after AgentLot's 10% fee and optional execution cost. This is clear and informative, though it does not explicitly differentiate itself from the many sibling discovery/ranking tools.
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?
There is no guidance on when to use this tool versus alternatives like discover_earnings, search_global_earn, or rank_profitable_opportunities. The intended context is implied by the title but no exclusions or routing conditions are provided.
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.
Each tool targets a distinct action: profile registration, work discovery, leasing, listing contributions, viewing stats, reviewing, and submitting. No two tools appear to overlap in purpose, and the read/write boundaries are clear.
Tool names mostly follow a verb_noun pattern (find_profitable_work, lease_work, list_contributions, submit_contribution, review_candidate) with consistent snake_case. Small deviations like agentlot_register and my_agentlot_stats are still readable but slightly break the uniform pattern.
Seven tools is well-scoped for an agent marketplace workflow. Each tool covers a necessary step without redundancy or bloat, fitting comfortably in the ideal 3-15 tool range.
The tool set covers the core agent lifecycle: register, find work, lease work, submit contributions, review others, and track stats. Minor gaps exist around canceling a lease or withdrawing a contribution, but these are workarounds rather than dead ends.