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alekskram

arcus-agent-gateway

Holder Snapshot

holder_snapshot
Read-only

Get top holder addresses, token balances, share percentages, and contract flags for a token symbol. Use to assess holder concentration and distribution.

Instructions

Top holders of a token's contract from the explorer (one page, max 50 rows, cached 600s inside the explorer client). Each row: address, value (float token units, raw/1e18), share_pct (= value / total_supply * 100) and is_contract. total_supply comes from the RPC totalSupply() call, falling back to the explorer's own token row when the RPC is unavailable (source-tagged either way). Explorer/RPC failures return an error dict with kind + hint, never a silent empty list. Example: holder_snapshot(symbol="AAPL", limit=10)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
symbolYes

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

A4.2/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/destructive annotations, the description discloses caching behavior (600s), pagination cap (50 rows), total_supply source and fallback, source tagging, and error behavior ('never a silent empty list'). This is rich, non-redundant behavioral context.

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: scope, data shape, supply calculation, fallback behavior, error semantics, and an example. It is front-loaded with the main purpose 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 two-parameter read-only tool with annotations and an output schema, the description covers the essential behavioral details: row contents, aggregation source, failure mode, caching, and row limits. Nothing needed 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 input schema has no descriptions (0% coverage), so the description carries the burden. It provides a concrete example with symbol and limit, and the 'max 50 rows' note clarifies the limit's effect. It stops short of explicitly defining each parameter's format and constraints, but an agent can infer correct usage.

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 clearly identifies the resource ('top holders of a token's contract') and adds concrete scope details such as one page, max 50 rows, and explorer sourcing. It is not a tautology and the meaning is immediately clear, but it does not explicitly distinguish itself from sibling tools like wallet_holdings or token_detail.

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 purpose statement and example ('holder_snapshot(symbol="AAPL", limit=10)') imply use when a token-holder snapshot is needed. However, there is no explicit guidance about when not to use it or which sibling tool should be preferred in related scenarios.

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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