search_ranked_marketMachine-ranked search using capability fit, reputation, price and latency.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| min_tier | No | ||
| capability | No | ||
| max_price_cents | No | ||
| max_dispute_rate | No | ||
| min_success_rate | No | ||
| max_latency_seconds | No | ||
| supported_languages | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile, so the bar is lower. The description adds the 'machine-ranked' behavior and its criteria, but doesn't explain what machine-ranking means, whether results are paginated, or how the ranking weights interact.
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?
A single 11-word sentence with zero filler. Every term (machine-ranked, capability fit, reputation, price, latency) carries signal and the core behavior is front-loaded.
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 8 undocumented parameters and no output schema, one sentence is insufficient. The description doesn't state the output format, the semantics of several filter parameters, or how this tool differs from the abundant sibling search tools.
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 carry the burden. It does map several parameters to ranking criteria — capability→capability fit, max_price_cents→price, max_latency_seconds→latency, min_success_rate/max_dispute_rate→reputation — but leaves limit, min_tier, and supported_languages entirely unexplained.
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?
States a specific verb and resource: 'Machine-ranked search' of the 'AI market', with explicit ranking criteria (capability fit, reputation, price, latency). It is clear what the tool does, though it does not differentiate from closely named siblings like search_global_market or search_listings, which also search markets.
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 guidance is given on when to use this tool versus the many search siblings in the namespace (search_global_market, search_listings, search_capability_suppliers, search_supply_graph). An agent must guess which search tool fits its intent.
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.