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by io.github.Schoasch
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Get Stablecoin Supply Trend

arena_get_stablecoin_supply

Aggregate stablecoin supply (crypto-liquidity proxy) — is the liquidity impulse turning or accelerating? macro_regime only gives the 30d delta; this exposes the trend: current supply, 30d/90d change (USD + %) plus a compact time series so direction and speed are visible, not just a single delta. Read impulse for what the supply change is doing — four states (accelerating / decelerating / reversal / flat). The neighbouring acceleration_usd is the signed difference last-30d minus prior-30d and gets LARGE exactly when the trend reverses, while the older boolean accelerating requires the same direction AND a bigger magnitude; a reversal therefore shows a big acceleration_usd next to accelerating: false. Source DefiLlama peggedUSD. [Free tier]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • removedInput schema / properties / context
      Removed value: -{
-  "description": "Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): \"Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization.\"",
-  "type": "string"
-}
    • removedInput schema / required
      Removed value: -[
-  "context"
-]
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations provided, the description fully carries the behavioral burden. It explains the output structure in detail: current supply, 30d/90d change in USD and %, a compact time series, the `impulse` four-state enum, and the precise semantics of `acceleration_usd` versus the boolean `accelerating`, including how a reversal looks in the data. This goes far beyond a simple 'returns supply' and gives the agent a mental model of the tool's behavior.

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

Conciseness5/5

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

The description is dense but efficient: it front-loads the core purpose, then unpacks the richer trend data, then explains the nuanced field interactions, and finishes with source and tier. Every sentence contributes new information—no fluff, no repetition. The structure flows logically from high-level purpose to detailed field semantics.

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 there is no output schema, the description must convey what the tool returns, and it does comprehensively. It lists the exact fields (current supply, 30d/90d change, time series, impulse, acceleration_usd, accelerating) and explains their meaning. It also notes the data source and free-tier context. For a parameterless read tool, nothing essential is missing.

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?

The tool takes zero parameters, and the schema confirms this (empty object, 100% coverage). Per the rubric baseline for 0 parameters, a score of 4 is appropriate. Since there are no parameters, the description cannot add parameter semantics, and it correctly omits any mention.

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 opens with a specific verb and resource (`Aggregate stablecoin supply`) and immediately establishes its role as a crypto-liquidity proxy. It explicitly distinguishes itself from the sibling `arena_get_macro_regime` by pointing out that macro_regime only provides a 30d delta, whereas this tool exposes the trend with current supply, changes, and a time series. The purpose is unmistakable and clearly differentiated.

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?

The description directly names the alternative (`macro_regime`) and explains its limitation (only the 30d delta), implicitly instructing agents to use this tool when they need trend direction/speed rather than a single delta. It also clarifies what each field (`impulse`, `acceleration_usd`, `accelerating`) means, giving clear context for when this data is relevant. The note about the free tier and source further aids usage decisions.

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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