DERIVATION TRUTH: raw upstream payloads, SHA-256-pinned, with the exact collector code hash for each fetch. Replay any published value: fetch the verify_url yourself, run the pinned code, compare. Answers 'a hash proves a footprint, not the derivation' — divergence from origin is mechanically detectable. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | No | Optional: filter to one symbol (e.g. AKT). Omit for the coverage list. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden, and it delivers meaningful behavior: data is raw and pinned, each fetch has an exact collector code hash, and divergence is mechanically detectable. The 'Free' note addresses cost. It doesn't mention read-only semantics, rate limits, or auth, but for a read-style ledger the key behavioral traits are disclosed.
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 packs useful information into four sentences, but the ALL-CAPS 'DERIVATION TRUTH:' opener and slogan-like 'Answers a hash proves a footprint, not the derivation' are rhetorical rather than information-dense. It is not bloated, but it is not as lean as it could be.
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 single-optional-parameter tool with no output schema, the description explains the nature and use of the ledger well. However, it never explicitly states the return shape (which fields appear in entries, where verify_url comes from) or any pagination or format behavior, leaving some burden on the agent to infer the structure.
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 only parameter, symbol, is fully documented in the schema with 'Filter to one symbol... Omit for the coverage list.' The description adds no additional parameter-level meaning, so the baseline 3 applies given 100% schema coverage.
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 identifies a specific resource — a ledger of raw upstream payloads with SHA-256 pins and collector code hashes — and implies a retrieval use case via the tool name. However, it is phrased as a tagline rather than a plain verb+resource statement, and it doesn't explicitly contrast with siblings like verify_footprint or get_zk_provenance.
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 concrete operational guidance: replay any published value by fetching verify_url, running the pinned code, and comparing. It also clarifies the conceptual distinction between hash footprints and actual derivation. But it doesn't name alternative tools or state when not to use this ledger, so it stops short of full routing guidance.
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.