Query records from a dataset with limit/offset. Free tier: 10 records per query. Purchase products or top up credits for more.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max records (max 10 for free tier) | |
| offset | No | Record offset | |
| dataset | Yes | Dataset name (e.g., depin_onchain) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description carries the full behavioral burden. It does disclose the free-tier limit and the need to purchase or top up credits for higher limits, which is valuable quota information. However, it does not cover response shape, error behavior, or consequences of exceeding the quota.
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 short sentences deliver the core action, the pagination concept, and the relevant quota information without waste. Every phrase earns its place and the most important action is stated first.
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?
For a simple read-style tool with 3 parameters and no output schema, the description is nearly complete: it conveys the operation, the limit mechanism, and the quota/charge behavior. It lacks minor context like a concrete return-format note, but nothing an agent needs to invoke it correctly is missing.
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 description coverage is 100%, so parameter meaning is already fully documented. The description only mentions limit/offset and reproduces the 10-record free-tier limit that the schema already states for 'limit', adding no new semantic detail beyond the structured definition.
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 states a specific verb ('Query') and resource ('records from a dataset'), and clarifies the pagination mechanism (limit/offset). It does not explicitly differentiate from sibling tools, but 'fetch_dataset' vs 'check_balance' is fairly self-evident.
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 gives no when-to-use guidance, no excluded scenarios, and no mention of when to prefer check_balance or other siblings. There is an implicit reading that this tool is for dataset record retrieval, but the free-tier/credit messaging concerns quota, not usage context.
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.