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TokenBank — tokenized real-world assets

How agents buy tokens

get_agent_buying_guide
Read-onlyIdempotent

Start here when building or running an AI agent or trading bot that buys tokens on its own. Returns the whole how-to as data: the six steps from wallet to confirmed purchase with the MCP/API call for each, the purchase paths (wallet only / owner onboarded once / platform account) with live counts, which asset classes a self-custody wallet can reach without an identity check, the highest-yield wallet-only candidates, the guardrails a wallet policy should enforce, and the guides that explain it (each with a JSON URL). Then call get_purchase_steps for one ticker.

Input Schema

NameRequiredDescriptionDefault
limitNoHow many candidates (default 10)
min_yield_pctNoOnly list candidates at or above this stated yield, in percent

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context by disclosing that the payload includes live counts and that each referenced guide carries a JSON URL — output characteristics not visible without an output schema. It stops short of noting payload size, caching, or whether 'live counts' are real-time, which keeps it at 4.

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 trigger condition is front-loaded in the first clause, and the middle sentence is a dense but information-bearing enumeration of the return payload. It is long and list-heavy, but each item maps to a distinct thing the agent needs to know before calling.

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

Completeness5/5

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

With no output schema, the description carries the full burden of explaining return values, and it does so item by item. Combined with the annotations covering the read-only/idempotent profile and the schema covering parameters, nothing an agent needs in order to select and call this tool is missing.

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 100%, so both parameters (limit, min_yield_pct) are already documented in the schema, making 3 the baseline. The description hints at the filtering behavior via "highest-yield wallet-only candidates" but adds no explanation of how limit or min_yield_pct shape that returned set.

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

Purpose5/5

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

The description names a specific resource (the agent buying guide) and enumerates exactly what it contains: six steps, purchase paths with live counts, permissionless asset classes, top wallet-only yields, guardrails, and linked guides. It is clearly distinguishable from siblings like get_purchase_steps, which it explicitly positions as the per-ticker follow-up.

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

Usage Guidelines5/5

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

"Start here when building or running an AI agent or trading bot that buys tokens on its own" sets an explicit trigger condition, and the closing sentence routes the agent to get_purchase_steps for single-ticker detail. Both when-to-use and when-to-use-something-else are stated directly.

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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