tld_stats
Bid rate and average appraisal for each domain extension with at least 400 listings. Shows which extensions actually attract bidders and which are dead inventory.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Bid rate and average appraisal for each domain extension with at least 400 listings. Shows which extensions actually attract bidders and which are dead inventory.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses a key behavior: results are filtered to extensions with at least 400 listings. It also frames output as actionable (attract bidders vs dead inventory). However, with no annotations, it does not cover aspects like data freshness, performance, or that the tool is read-only, leaving some behavioral traits unspecified.
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 sentences with zero waste. The first sentence states the core function and condition, the second adds interpretive value. Well 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?
Given no output schema, the description sufficiently explains the output (bid rate and average appraisal per TLD) and the minimum listing threshold. Minor gap: it does not enumerate the exact fields returned, but this is acceptable for a simple, no-parameter tool.
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 coverage is 100% with no parameters, so baseline is 4. The description adds meaning by explaining what the tool returns (bid rate, average appraisal) and the filtering threshold, going beyond the empty schema.
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 the tool provides 'bid rate and average appraisal for each domain extension with at least 400 listings.' It uses specific verbs and identifies the resource (domain extensions), distinguishing it from sibling tools like bid_rates (likely per-domain) and market_stats (general statistics).
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 usage for assessing TLD performance and identifying active vs dead inventory, but it does not explicitly state when to use this tool over siblings or provide any exclusions. With no parameters, the context is straightforward, but explicit guidance is missing.
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.