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Glama

whale_transfer_alerts

$0.09 via x402: live whale alerts — the largest recent USDC / WETH / cbBTC / USDT / WBTC transfers on Base or Ethereum, each with USD value, amount, from/to wallets (contract? named? scam-flagged?), method, tx hash and age in seconds. The smart-money / whale-watching flow feed trading and copy-trade agents poll to catch big money moving — a Nansen-style whale alert without the subscription. Each whale chains into smart_money_wallet_activity and wallet_portfolio. Live from Blockscout.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chainNobase|ethereum (default base)
limitNo1-50, default 15
tokenNousdc|weth|cbbtc (base) or usdc|usdt|weth|wbtc (ethereum), or any ERC-20 contract 0x... (default usdc)
min_usdNoMinimum transfer size in USD (default 10000)
x_paymentNo

Schema Changelog

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

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It discloses the live feed nature, the $0.09 via x402 cost, the Blockscout source, the wallet labeling dimensions (contract, named, scam-flagged), and the output fields. It does not mention rate limits or pagination, but it covers the key operational traits for a read-only feed.

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 information-dense without being bloated: it covers purpose, use case, cost, output fields, source, and related tools in under 100 words. The Nansen-style phrase is somewhat promotional but adds a useful conceptual anchor.

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 read-only feed tool with no output schema and no annotations, the description is thorough: it lists the output fields, supported networks and tokens, cost, live behavior, and how to chain into sibling tools. There is no output schema to clarify return structure, so the description's field list is essential and present.

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 80%, so the schema already documents chain, limit, token, and min_usd well. The description mentions supported token types and chains, which mostly repeats schema content rather than adding new parameter semantics. The x_payment parameter remains undocumented, but the high baseline keeps this at 3.

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 that this tool returns live whale alerts for the largest recent transfers of specific tokens on Base or Ethereum. It names the exact data fields returned and distinguishes itself from related tools by describing how each whale chains into smart_money_wallet_activity and wallet_portfolio.

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 gives clear usage context: it is positioned as a smart-money/whale-watching feed for trading and copy-trade agents to poll. It also hints at related tools, but it does not explicitly state when not to use it or specify an alternative condition.

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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TDQS

C2.7/5.0
Disambiguation2/5

Many tools occupy the same conceptual space: web_scrape vs markdown_web_scraper, post_check vs brand_ai_visibility_check, llm_chat_completions vs post_api_v1_chat_completions, chain_transaction_status vs chain_confirmations, and connect_token vs token_security_check + dex_token_data. Descriptions help in places, but for an agent facing 92 tools these near-overlapping endpoints will frequently cause misselection.

Naming Consistency2/5

Everything is snake_case, but the conventions diverge sharply: get_chain_* and chain_* coexist for the same RPC family, post_* names are HTTP-route artifacts, api_generate reverses noun_verb order, and many names are bare nouns rather than verb_noun. There is no predictable naming pattern an agent can rely on.

Tool Count1/5

At 92 tools this is far beyond the range where an agent can keep the surface coherent, even for a store. The flat tool list mixes products, bundles, aliases, proxies and single-use verticals, so most of the count is noise for any given task. A catalog/search/payment model with fewer exposed tools would fit the storefront purpose better.

Completeness3/5

The server has impressive breadth and covers key storefront/market workflows: catalog, samples, credits, directory listing, notary, and the task lifecycle. But each domain is shallow: there is no chain transaction broadcast, no task update/cancel/dispute, no AI-visibility history, and many verticals are a single tool with no follow-on operation. The surface is broad but not deeply complete.