Register AI, human or hybrid worker
register_workerRegister a worker/capability endpoint for the universal work economy.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| regions | No | ||
| worker_type | Yes | ||
| capabilities | No | ||
| display_name | Yes |
register_workerRegister a worker/capability endpoint for the universal work economy.
| Name | Required | Description | Default |
|---|---|---|---|
| regions | No | ||
| worker_type | Yes | ||
| capabilities | No | ||
| display_name | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description only says 'register' and adds no information about side effects, idempotency, authorization requirements, or what happens after registration. The annotations show readOnlyHint=false and destructiveHint=false, but this does not reveal meaningful behavioral details for a mutation tool.
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 a single sentence with no wasted words and the action is front-loaded. However, it is so sparse that it sacrifices the semantic detail needed for a 4-parameter tool with no schema descriptions.
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 registration tool with no output schema and no parameter descriptions, the description should explain required inputs, optional fields, and expected behavior. It only offers the vague phrase 'for the universal work economy' and leaves critical operational details missing.
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, but it only loosely hints at worker types and does not explain 'capabilities', 'regions', or 'display_name'. The title also excludes 'api' and 'compute' from the enum, creating ambiguity about valid worker_type values.
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 action ('Register') and a resource ('worker/capability endpoint'), which gives a basic sense of what the tool does. However, it does not differentiate it from the closely named siblings register_agent and register_capability, and the title's 'AI, human or hybrid' omits the 'api' and 'compute' values allowed by the worker_type enum.
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 gives no guidance about when to use this tool instead of alternatives like register_agent, register_capability, or discover_compute. With many sibling tools present, an agent is left to infer the correct choice from the name alone.
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.