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get_tokens

Return the top-N tokens by trades_30d, joined against attestation tier + warning flags. Mirrors /tokens.json's data path (token_category_current JOIN token_facts). Third-party- naming — data payload includes dispute_contact_url. Wrapped in the proof envelope.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of behavioral disclosure. It adds useful context: the data is joined against attestation tier and warning flags, the payload includes a third-party-named field `dispute_contact_url`, and the result is wrapped in a proof envelope. However, it does not disclose whether the operation is read-only (likely, but unstated), whether the proof envelope requires verification, or any rate-limit or pagination behavior. The description adds some value but leaves meaningful behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded with the core purpose ('Return the top-N tokens by trades_30d'), followed by relevant data-path and payload context. The multi-line formatting is slightly awkward but every sentence earns its place; no filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter read-style tool, the description covers the core purpose, the data source, and the payload shape. However, it lacks explicit guidance on the `limit` parameter semantics, the proof envelope's role (e.g., whether the agent must verify it), and any distinction from sibling tools like get_token_attestation. The absence of an output schema raises the burden, and the description does not fully meet it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for the single `limit` parameter. The description mentions 'top-N tokens', which implies the `limit` parameter controls N, but it does not explicitly state that `limit` maps to N, nor does it describe the default or allowed range. The description adds partial meaning but does not fully clarify the parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Return'), a resource ('top-N tokens'), and a clear ordering criterion ('by trades_30d'), and it distinguishes the data path from the sibling get_token_attestation by mentioning the join against attestation tier and warning flags. It does not explicitly name a sibling alternative, but the scope is clear enough to separate it from the other get_* tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by describing the data path and the proof envelope, but it does not explicitly state when to use this tool versus alternatives like get_token_attestation or get_signed_snapshot. There is no when-not-to-use guidance, so an agent must infer the appropriate context from the data-path details.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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