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whale-insights

by com.cryptowhaleinsights

analysts

Analyst personas with live performance stats — Returns all 10 pseudonymous CryptoWhaleInsights analyst personas — ChainHawk (BTC & Macro On-Chain), WhaleWatch (Multi-Chain Whale Tracking), AlphaScout (Emerging Tokens & Narratives), DeFiPulse (DeFi, Stablecoins & Bridges), QuantEdge (Signal Performance & Risk), RateHawk (Funding Rates & Derivatives), FlowTracer (Stablecoin & Capital Flows), UnlockGuard (Token Unlock Risk & Recovery), SentimentEdge (Social Sentiment Extremes), NarrativePulse (Sector Rotation & Narratives). These are algorithmic signal-attribution identities, not human analysts: every signal generated by the platform's on-chain monitoring engine is automatically attributed to the analyst whose domain matches the alert type and chain. Stats are 100% real — computed from the live signalHistory PostgreSQL table using the same resolved-signal logic as the Signal Performance Proof page. winRate is a fraction (0.71 = 71% win rate); avgReturn is a percentage (12.3 = +12.3% average return per signal). B

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
analystNoOptional analyst slug filter. When provided, only the matching analyst is returned. One of: chain_hawk, whale_watch, alpha_scout, defi_pulse, quant_edge.

TDQS

A3.5/5.0
Behavior4/5

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

The description explains that stats are live, computed from signalHistory, and clarifies the format of winRate and avgReturn. It also notes that these are algorithmic identities, not human analysts, which is important 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.

Conciseness2/5

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

The description is overly verbose, listing all 10 analyst names and their domains, which could be summarized. Not every sentence is essential; the length may reduce readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description adequately explains return values (personas, stats format) and data source. It is fairly complete for the tool's purpose.

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 coverage is 100%, so baseline is 3. The description adds minimal extra meaning beyond the schema's enum and description for the optional 'analyst' parameter.

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 the tool returns all 10 pseudonymous analyst personas with live performance stats. It also names each analyst, distinguishing from sibling tools that focus on signals or top performers.

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

Usage Guidelines2/5

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

The description does not provide explicit guidance on when to use this tool versus alternatives like analysts_signals or analysts_top. Usage context is implied but not explicitly differentiated.

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

A3.8/5.0
Disambiguation4/5

Most tools have distinct purposes, but there are a few pairs with overlapping boundaries (e.g., analysts_signals vs analysts_signals_all, whale_movements vs whale_movements_summary) that could cause minor confusion.

Naming Consistency4/5

Naming is predominantly snake_case and descriptive, with minor inconsistencies in plural/singular forms (e.g., 'analysts' vs 'analyst_archive'). Overall pattern is stable.

Tool Count2/5

With 55 tools, the server is quite heavy. While the scope is broad, many tools are history/monthly variants that could be combined, making the count feel inflated beyond what is ideal for a single server.

Completeness4/5

The tool set covers a wide range of crypto analytics domains (analysts, arbitrage, funding, whales, sentiment, etc.). Minor gaps exist (e.g., no direct token price endpoint), but overall it's a comprehensive surface.

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