ONBOARDING SPEC: freshness, history depth, coverage and endpoints per feed (depin_onchain, depin_daily, health_profiles, signal_ledger, market_ticks, zk_footprints). Machine-legible; answers 'how stale is this and how far back does it go' for every feed. Free, no key.
| 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. It explicitly discloses 'Free, no key' and 'Machine-legible', which are useful behavioral notes. However, it does not mention response structure, caching, or potential latency, so it is not fully transparent but above average.
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 sentence that front-loads the purpose with 'ONBOARDING SPEC' and efficiently lists the key metadata dimensions and feeds. It avoids extraneous phrasing and earns every word.
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 and no parameters, the description covers the content types but not the exact structure of the response (e.g., whether it is a mapping of feed to metadata, keys, or value formats). 'Machine-legible' hints at structure but does not fully specify it. An agent may need additional context to parse the output correctly.
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, so the schema is trivially complete. Per rubric, 0 parameters warrants a baseline of 4. The description adds no parameter-specific meaning because none exist, so the baseline holds.
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 an onboarding spec with freshness, history depth, coverage, and endpoints for six named feeds. It answers a specific question about staleness and historical depth, and its unique metadata purpose distinguishes it from sibling tools like list_datasets or fetch_dataset.
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 used when an agent needs freshness or history metadata for a feed, but it does not explicitly state when to use it versus alternatives, nor does it mention when not to use it. It lacks direct comparison to siblings or exclusions, leaving some inference to the agent.
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.
Most tools target distinct actions (browse, query, purchase, top-up), but get_zk_provenance and verify_footprint both promise cryptographic verification for datasets/records and could easily be confused. fetch_dataset vs get_stats also has mild overlap, but the descriptions clarify querying records vs. aggregate statistics.
All tools follow a consistent snake_case verb_noun pattern: check_balance, fetch_dataset, list_products, purchase_data, register_agent, etc. The verbs are action-oriented and the nouns correspond to the resource being handled.
Ten tools cover registration, billing, product browsing, data querying, stats, and verification without feeling bloated. The count is typical for a marketplace/API server and each tool serves a concrete step in the user journey.
The core lifecycle is complete: register, view credits, browse products, buy, query, verify, and top up. Minor gaps exist around post-purchase management, such as listing owned subscriptions/entitlements or managing/rotating the API key, but agents can complete the main workflows without dead ends.