search_global_earnSearch normalized AgentLot + external BUY-side jobs and bounties, ranked by expected profit. No job is automatically claimed and earnings are never guaranteed.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | No | ||
| currency | No | ||
| funded_only | 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 important behavioral context beyond the annotations: no job is automatically claimed and earnings are never guaranteed, which sets accurate expectations for a search that surfaces potential income.
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 concise sentences with no filler. It front-loads the core action and relevant caveats, making it easy to scan and act upon.
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?
Although annotations cover safety, there is no output schema and the description does not explain the return structure or parameter behavior. Given 0% schema description cooverage, the description alone is not enough for an agent to know how to narrow results (e.g., via limit or currency) or what fields to expect in the ranked result set.
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%, and the description provides no information about the parameters (limit, query, currency, funded_only) or their formats/semantics. The agent must infer everything from parameter names alone, which is especially risky for currency and funded_only.
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 clearly states a specific action, resource, and scope: it searches normalized AgentLot and external BUY-side jobs and bounties, ranked by expected profit. This specificity distinguishes it from broad sibling tools like search_global_market or search_listings, even though no sibling is named.
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 implies when to use the tool: when searching for paid work or bounties across multiple sources. However, it provides no explicit guidance about when not to use it or which alternative sibling (e.g., search_external_earn, find_funded_work) would be better suited for a particular need.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.