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whale-insights

by com.cryptowhaleinsights
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analysts

Read-onlyIdempotent

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.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
analystsNo
updatedAtNo
attributionNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "analysts": {
+      "items": {
+        "properties": {
+          "alertTypes": {
+            "items": {
+              "description": "Signal alert types routed to this analyst (whale_move, volume_breakout, accumulation, smart_money_loading, fear_buy, whale_convergence)",
+              "type": "string"
+            },
+            "type": "array"
+          },
+          "bio": {
+            "description": "Short description of the analyst's focus",
+            "type": "string"
+          },
+          "color": {
+            "type": "string"
+          },
+          "icon": {
+            "type": "string"
+          },
+          "id": {
+            "description": "Unique analyst slug. One of: chain_hawk, whale_watch, alpha_scout, defi_pulse, quant_edge, rate_hawk, flow_tracer, unlock_guard, sentiment_edge, narrative_pulse",
+            "type": "string"
+          },
+          "name": {
+            "description": "Display name, e.g. ChainHawk",
+            "type": "string"
+          },
+          "specialization": {
+            "description": "Domain covered, e.g. BTC & Macro On-Chain",
+            "type": "string"
+          },
+          "stats": {
+            "description": "Live performance stats computed from signalHistory DB. Null values mean < 5 resolved signals.",
+            "properties": {
+              "avgReturn": {
+                "description": "Average return in percentage points (e.g. 12.3 = +12.3%). Null if < 5 resolved signals.",
+                "nullable": true,
+                "type": "number"
+              },
+              "lastSignalAt": {
+                "format": "date-time",
+                "nullable": true,
+                "type": "string"
+              },
+              "losses": {
+                "type": "number"
+              },
+              "resolvedSignals": {
+                "description": "Signals with a win/loss outcome determined",
+                "type": "number"
+              },
+              "totalSignals": {
+                "description": "Total signals ever attributed to this analyst",
+                "type": "number"
+              },
+              "winRate": {
+                "description": "Fraction (0–1). Multiply by 100 for %. Null if < 5 resolved signals.",
+                "nullable": true,
+                "type": "number"
+              },
+              "wins": {
+                "type": "number"
+              }
+            },
+            "type": "object"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "attribution": {
+      "$ref": "#/components/schemas/Attribution"
+    },
+    "updatedAt": {
+      "format": "date-time",
+      "type": "string"
+    }
+  },
+  "type": "object"
+}
  2. Added

TDQS

A4/5.0
Behavior5/5

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

The description goes well beyond the read-only annotations by explaining that analysts are algorithmic attribution identities rather than humans, that stats are computed from the live signalHistory PostgreSQL table with resolved-signal logic, and that winRate is a fraction while avgReturn is a percentage. This gives the agent a strong behavioral and data-integrity picture.

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 front-loaded with its core purpose and every clause adds meaningful context, including domain areas and metric units. The list of ten personas is long but necessary for identification; the only structural defect is the unexplained trailing 'B', which appears to be a truncation artifact.

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

Completeness3/5

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

With annotations and an output schema present, the description covers data provenance, non-human attribution, and metric semantics well. However, it does not address the mismatch between the 10 named personas and the 5 enum values, nor does it provide guidance for choosing this over sibling analyst tools, so the picture is not fully complete.

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?

The schema fully documents the optional analyst slug filter, so the baseline is 3. The description adds useful persona context but does not map friendly names to schema slugs, and it claims all 10 personas while the enum only exposes 5 slugs, which creates ambiguity about whether the other five can be filtered.

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 uses a specific verb ('Returns') and resource ('all 10 pseudonymous CryptoWhaleInsights analyst personas') and then enumerates all ten with their domains. This clearly distinguishes the tool from signal-level siblings and makes the scope immediately obvious.

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 phrase 'live performance stats' and the reference to the Signal Performance Proof page imply this is for current analyst-level performance snapshots, but no alternative tools are named and no when-to-use/when-not-to-use conditions are given. Sibling names like analysts_top and analyst_daily_summary suggest alternatives, but the description itself does not route the agent.

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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