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whale_activity

Read-only

Detect recent large wallet movements for a token (whale tracking)

Use when: You need to detect large token transfers that may signal institutional moves, accumulation, or sell-offs. Not for: you need one address's holdings — wallet_balance; exchange order-book size rather than on-chain transfers — orderbook_depth. Ethereum ERC-20 transfers only. Returns: large_transfers[] with from, to, amount, usd_value, minutes_ago; total_volume_usd Example response: {"token":"USDC","large_transfers":[{"from":"0xabc...1234","to":"0xdef...5678","amount":5000000,"usd_value":5000000,"minutes_ago":12},{"from":"0x111...aaaa","to":"0x222...bbbb","amount":2500000,"usd_value":2500000,"minutes_ago":34}],"total_volume_usd":7500000,"source":"etherscan"}

Price: $0.000 USDC per call

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tokenYesToken symbol to track
min_usdNoMinimum transaction size in USD

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

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 and openWorldHint=true, so the safety profile is covered. The description adds real value beyond that: it scopes the data to 'Ethereum ERC-20 transfers only', names the data source (etherscan in the example), documents the return shape, and discloses a per-call price. Rate limits and refresh/latency semantics are not stated, but coverage is well above the annotation baseline.

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?

Well front-loaded and sectioned (purpose, Use when, Not for, Returns, Example). Every block earns its place, though the worked example response is somewhat long relative to a two-parameter tool. No filler or repetition.

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?

There is no output schema, so the description correctly carries the return contract, listing large_transfers[] fields (from, to, amount, usd_value, minutes_ago) and total_volume_usd, plus a concrete example. An agent has everything needed to call and interpret this tool.

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 (token, min_usd) are already fully documented in the schema. The description implies the threshold behaviour through 'large transfers' and usd_value, but adds no syntax, default value, or format detail beyond the schema. Baseline 3 is correct when the schema does the heavy lifting.

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?

States a specific verb and resource: 'Detect recent large wallet movements for a token (whale tracking)'. An agent can immediately tell this apart from wallet_balance and orderbook_depth, which the description names as non-overlapping alternatives.

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?

Explicit 'Use when' and 'Not for' clauses with named sibling alternatives ('one address's holdings — wallet_balance; exchange order-book size rather than on-chain transfers — orderbook_depth'). The routing decision is fully specified with no inference required.

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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