Ecosystem-wide x402 totals from the daily market scan, next to the live challenge, payment and revenue counters of one shop that actually runs paid endpoints and publishes what they earn. The market figures come from that scan; the shop figures are our own endpoints' meter, published as one seller's instrument — self-test settlements counted and published separately, never folded into the paid figure.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does add useful transparency by stating that self-test settlements are counted and published separately and never folded into the paid figure, and it distinguishes market-scan data from the shop's endpoint meter. However, it does not explicitly state whether the tool is read-only, whether any permissions are needed, or what side effects (if any) exist.
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 dense, comma-laden sentence with an em-dash aside, and it uses ornate, long-winded phrasing. Core information like 'market totals' and 'one shop's counters' is present, but the style obscures it, and the self-test clarification is repeated with slightly different wording. A shorter, front-loaded version would be more effective.
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?
There is no output schema and no annotations, so the description should explain what the tool returns. It covers data provenance and the self-test separation, but omits the actual fields, time range, units, and response format. For a zero-parameter stats tool, an agent still cannot predict what data will be in the result.
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?
The input schema has zero parameters, and schema description coverage is 100%, so there are no parameter semantics to clarify. Per the baseline for a tool with no parameters, a score of 4 is appropriate; the description does not need to add parameter information.
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 conveys that the tool surfaces ecosystem-wide x402 totals alongside a single shop's payment and revenue counters, which gives a sense of content. However, it lacks a direct verb like 'fetch' or 'report,' and the narrative style obscures the exact action the tool performs. Differentiation from sibling tools is only implicit through 'one shop' vs market-wide figures, but no sibling is explicitly contrasted.
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?
There is no explicit guidance on when to use this tool versus alternatives such as bazaar_pulse or crawler_watch. The description explains data provenance and the self-test settlement separation, but these are data-quality clarifications, not usage instructions. The agent is left to infer any selection criteria from the tool name and content.
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 clearly distinct observational domain: catalogue changes, crawler traffic, experiment scoreboard, provider ranking, and ecosystem stats. There is no overlap in purpose or output, and the descriptions reinforce their boundaries.
Four tools follow a noun_noun pattern (bazaar_pulse, crawler_watch, experiment_scoreboard, market_stats), while find_provider uses verb_noun, a minor deviation. All names are lowercase snake_case, keeping the style consistent overall.
Five tools is well-scoped for an observatory, providing a focused yet appropriately broad surface. Each tool earns its place, and the count is typical for a domain-specific server.
The set covers market-level stats, catalogue changes, crawler activity, experiment tracking, and provider discovery, covering the core observational workflows. Minor gaps exist around per-provider history or raw snapshot access, but agents can work around these.