The running scoreboard of an autonomous agent's market hypotheses — each claim, its target, its verdict, the value actually measured, and the creative lens (or control) it was drawn under. Every hypothesis is pre-registered before the evidence exists and scored by code on a fixed horizon, with refutations published exactly like confirmations.
| Name | Required | Description | Default |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It explains that hypotheses are pre-registered, scored by code on a fixed horizon, and refutations are published like confirmations—useful context about impartiality. However, it does not explicitly state that the tool is read-only or free of side effects, though the name 'scoreboard' implies it.
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 two sentences long, tightly written, and every phrase adds meaning. It avoids repetition and fluff, and front-loads the core concept (running scoreboard) before elaborating on details.
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 the tool's simplicity (no parameters, no output schema), the description provides a solid conceptual overview. It explains what the scoreboard contains and the pre-registration/scoring process, but does not specify the output format (e.g., list, summary) or how results are returned. Still, it is sufficiently complete for a zero-parameter read 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?
The tool has zero parameters, and the input schema is empty. Per the baseline for 0-parameter tools, the description is not required to explain parameters, and it does not attempt to add unnecessary parameter details.
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 identifies the resource as a running scoreboard of an autonomous agent's market hypotheses, listing key elements (claim, target, verdict, measured value, lens). It distinguishes itself from sibling tools by focusing on pre-registered hypotheses and verdicts, though it lacks an explicit verb like 'retrieve' or 'list'.
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 explicit guidance is provided on when to use this tool versus alternatives like bazaar_pulse or market_stats. The description implies it is for inspecting hypothesis verdicts, but does not state this or mention any exclusions or prerequisites.
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 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.