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kuyen-labs

Fuul MCP Server

by kuyen-labs

list_price_references

Discover price reference currencies for token-holder triggers when the held token is unknown. Filter by chain to match decimals and use as volume_currency_expression.

Instructions

Lists currencies usable as price references for token-holder triggers: GET /api/v1/currencies?price_reference=true&page_size=100. Optional chain_identifier (e.g. "ethereum") filters to that chain. Each result includes identifier (use as volume_currency_expression), name, decimals, chain_identifier. REQUIRED before create_trigger when the held token may not be a known asset (not on CoinGecko/CMC). Workflow: (1) Call with the trigger chain. (2) If token_address is in results[] (EVM: compare identifier case-insensitively), set volume_currency_expression = token_address. (3) If NOT listed, ask the user: stablecoin or variable-price? How many decimals (6 or 18)? Pick a reference from results with matching decimals. Examples: DAI 0x6b175474e89094c44da98b954eedeac495271d0f on Ethereum is listed — use same address. Unknown 18-decimal stablecoin 0xa3931d71877C0E7a3148CB7Eb4463524FEc27fbD on Ethereum is NOT listed — use DAI as volume_currency_expression, not the token address. Wrong reference → trigger creates (201) but never prices volume correctly. Params: {} or {"chain_identifier":"ethereum"}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chain_identifierNoFilter by chain identifier (e.g. "ethereum", "arbitrum"). Match list_chains / currency API.
Behavior5/5

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

Although no annotations exist, the description fully covers behavioral traits: it explains the API call, optional filtering, result structure, and crucial behavioral consequence that a wrong reference leads to a trigger that never prices correctly. It also clarifies no destructive actions and sets expectations.

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 relatively long but well-structured: it starts with the core purpose, then adds filtering, result format, workflow, and examples. Every sentence serves a purpose, though some could be slightly compressed without losing information.

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?

Given no output schema, the description fully explains what results contain (identifier, name, decimals, chain_identifier). It also provides a complete workflow, prerequisites, and error examples. The tool's role as a prerequisite is clearly established, and all necessary information for correct invocation is present.

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

Parameters4/5

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

Schema coverage is 100% for the single parameter, and the description adds value by providing examples of usage ('ethereum'), referencing where the chain_identifier comes from (list_chains), and illustrating both with and without the parameter. This goes beyond the bare schema description.

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 clearly states the tool lists currencies usable as price references for token-holder triggers, specifies the API endpoint, and explains its role as a prerequisite for create_trigger. It distinguishes itself from related tools by providing workflow context, and the detailed examples enhance clarity.

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?

Explicitly states when the tool is required (before create_trigger when token is unknown), provides a step-by-step workflow, includes concrete examples of correct and incorrect usage, and warns about consequences of wrong references. It implicitly advises when to use alternatives (if token is known).

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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