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alekskram

arcus-agent-gateway

Wallet Holdings

wallet_holdings
Read-only

Get a wallet's tokenized equity holdings by address, showing symbol, name, token value, estimated USD position, and portfolio total from cached quotes, with a note when prices are missing.

Instructions

Which tokenized equities (out of the assets() universe) a wallet holds, from the explorer's address token-balances. The universe join is case-insensitive on the contract address; rows: symbol, name, value (float token units, raw/1e18). est_position_usd and portfolio_usd_total are computed ONLY from quotes already in the price cache (no quote fan-out); when quotes are missing or stale a note says so instead of faking numbers. Results cached 120s. Example: wallet_holdings( address="0x8366a39CC670B4001A1121B8F6A443A643e40951")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
addressYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.2.0

TDQS

A4.7/5.0
Behavior5/5

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

The description adds substantial behavioral detail beyond the read-only annotations: quote values are computed only from the price cache with no quote fan-out, missing/stale quotes are surfaced via a note, results are cached for 120 seconds, and the universe join is case-insensitive. This is excellent transparency for an agent.

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 every sentence adds value: purpose, data source, schema fields, units, caveats about quote staleness, cache behavior, and an example. It is front-loaded with the main operation and contains no filler.

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?

For a single-parameter, read-only tool with an output schema, this description covers all essential operational context: return rows, units, computation constraints, stale-data handling, caching, and a concrete example. Nothing necessary for correct invocation 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 schema only defines one string parameter with no description, so the description must compensate. It does so by clarifying the wallet-address context and providing a realistic example address, though it leaves explicit format/validation details implicit.

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 what the tool returns: tokenized equity holdings for a wallet, scoped to the assets() universe and sourced from explorer token balances. It gives enough detail (rows, units, join behavior) to distinguish it from sibling tools like holder_snapshot or transfer_history.

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

Usage Guidelines4/5

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

The description establishes a clear use case—querying a wallet's tokenized equity positions—and includes a concrete call example. It does not explicitly state when not to use it or name alternatives, but the context is strong enough for an agent to infer appropriate usage.

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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