Headline figures for the GoDaddy auction market today: how many domains are listed, how many carry a bid, median appraisal, and the measured CEILING on how often a listed domain sells. The sell-through figure is an upper bound and not a rate: a seller withdrawing a listing looks identical to a sale in this feed. Do not quote it as the probability a domain will sell.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility. It discloses key behavioral traits: the sell-through figure is an upper bound (not a rate) because withdrawals appear as sales, and it warns against misinterpreting it as a probability. This is valuable transparency beyond a simple listing.
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?
Three sentences with no filler. The purpose is stated first, followed by the list of metrics and a critical caveat. Every sentence earns its place.
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?
No output schema exists, but the description enumerates the main metrics and clarifies the tricky sell-through interpretation. It could be more explicit about the exact field names or data types, but for a simple aggregate snapshot, the coverage is sufficient.
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?
There are zero parameters, and the schema coverage is 100% (empty). The description adds meaning by explaining what the output metrics represent, which is sufficient for this parameterless tool.
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 specifies a clear verb ('provides headline figures') and resource ('GoDaddy auction market today'), and lists concrete metrics (domains listed, with bids, median appraisal, sell-through ceiling). It is distinct from sibling tools like bid_rates or list_auctions, which focus on rates or individual auctions.
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 the tool is for a quick market snapshot, but it does not explicitly state when to prefer this tool over alternatives (e.g., bid_rates for detailed rates, lookup_domain for a single domain). No when-not-to-use guidance is 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 clearly distinct aspect of GoDaddy domain auction data: market overview, per-TLD stats, bid rate analysis, live auction listings, and single-domain lookup. There is no overlap in purpose.
Tool names are descriptive but inconsistent in pattern: 'list_auctions' and 'lookup_domain' use verb_noun, while 'bid_rates', 'market_stats', and 'tld_stats' use noun_noun. The mix of imperative and declarative styles could confuse an agent about which tools are actions vs. data queries.
With 5 tools, the server is well-scoped for its domain. Each tool provides a distinct function without redundancy, and the count is neither too thin nor too heavy for a specialized domain auction data server.
The tool set covers the essential operations for querying GoDaddy auction market data: aggregate stats, per-TLD breakdown, bid rate insights, a live auction list, and individual domain lookup. No obvious gaps exist for the stated purpose.