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?
No annotations are provided, so the description must cover behavioral traits. It discloses that the tool is free and requires no key, and that output is 'machine-legible'. However, it does not explicitly state it is a read-only operation with no side effects, nor does it describe the return format beyond 'machine-legible'. It also doesn't mention any rate limits or errors. The disclosure is partial but useful.
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 packs the essential information (what it covers, which feeds, and the free/no-key aspect) without fluff. It is front-loaded with 'ONBOARDING SPEC' which sets context. Could be more structured (e.g., bullets for feeds) but is concise and effective for its purpose.
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 tool with no parameters and no output schema, the description gives enough to call it correctly: it explains what the tool provides (freshness, history depth, coverage, endpoints) and explicitly lists the feeds it covers. It omits the exact output structure, but 'machine-legible' and the stated questions it answers ('how stale is this and how far back does it go') give a clear mental model. It is complete for a metadata retrieval 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?
With 0 parameters, the baseline is 4. The description adds no parameter semantics because there are no parameters to explain. The empty schema is fully covered trivially, so the description does not need to compensate for missing parameter documentation. It correctly focuses on what the tool returns rather than parameters.
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+resource: it provides a data dictionary spec covering freshness, history depth, coverage, and endpoints per named feed. It clearly distinguishes itself from sibling tools like fetch_dataset (which fetches data) or list_datasets (which lists datasets) by focusing on metadata properties. The list of feed names makes the scope unambiguous.
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: it is an 'ONBOARDING SPEC' for checking metadata like staleness and history depth. It doesn't explicitly state when to use this versus alternatives (e.g., 'use this to get metadata, use fetch_dataset to get data'). No exclusions or conditions are mentioned. The purpose is clear, but the 'when vs when-not' guidance is left to inference.
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 (register, list, purchase, fetch, verify). The only ambiguity is between get_zk_provenance and verify_footprint, both providing cryptographic proofs, but they serve slightly different purposes (record provenance vs dataset footprint).
All tools follow a consistent snake_case verb_noun pattern (e.g., list_datasets, purchase_data, check_balance), making the set predictable.
10 tools is appropriate for the server's scope, covering user management, data browsing, purchasing, and verification without redundancy.
The set supports the full workflow: register, browse products/datasets, purchase, fetch data, check/top-up credits, and verify provenance. Minor gap: no way to view purchase history or account details, but core functionality is complete.