whale-insights

signal_history_monthly

Read-onlyIdempotent

Analyst signal performance permanent monthly archive — Returns the permanent monthly archive of analyst signal performance — one row per analyst per calendar month, aggregated from signal_history before months age out. Never deleted; covers all 10 CryptoWhaleInsights analysts (chain_hawk, whale_watch, alpha_scout, defi_pulse, quant_edge, rate_hawk, flow_tracer, unlock_guard, sentiment_edge, narrative_pulse). Optional ?analystId=chain_hawk to filter by a single analyst. Each row includes: month, analystId, totalSignals, winCount, lossCount, neutralCount, winRate (0–1 fraction), avgReturn (%, wins only), topSignalType, daysInMonth. Months with fewer than 5 signals are excluded. No authentication required. 60 req/min. 5-min cache. — Use this for long-term monthly archive data; use the corresponding live or daily-history tool for current or finer-grained data.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`analystId`	No	Analyst ID to filter by (e.g. chain_hawk, whale_watch, alpha_scout, defi_pulse, quant_edge, rate_hawk, flow_tracer, unlock_guard, sentiment_edge, narrative_pulse). Omit for all analysts.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`total`	No
`months`	No
`analystId`	No
`updatedAt`	No
`dataSource`	No
`attribution`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 3, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "analystId": {
+      "nullable": true,
+      "type": "string"
+    },
+    "attribution": {
+      "$ref": "#/components/schemas/Attribution"
+    },
+    "dataSource": {
+      "type": "string"
+    },
+    "months": {
+      "items": {
+        "properties": {
+          "analystId": {
+            "type": "string"
+          },
+          "avgReturn": {
+            "description": "Average return % on winning signals.",
+            "type": "number"
+          },
+          "daysInMonth": {
+            "type": "integer"
+          },
+          "lossCount": {
+            "type": "integer"
+          },
+          "month": {
+            "format": "date",
+            "type": "string"
+          },
+          "neutralCount": {
+            "type": "integer"
+          },
+          "topSignalType": {
+            "type": "string"
+          },
+          "totalSignals": {
+            "type": "integer"
+          },
+          "winCount": {
+            "type": "integer"
+          },
+          "winRate": {
+            "description": "Win rate as a fraction 0–1 (multiply by 100 for %).",
+            "type": "number"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total": {
+      "type": "number"
+    },
+    "updatedAt": {
+      "format": "date-time",
+      "type": "string"
+    }
+  },
+  "type": "object"
+}

AddedJun 26, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already cover the read-only, open-world, idempotent, and non-destructive profile. The description adds substantial non-obvious behavior: aggregation before months age out, never-deleted retention, exclusion of months with fewer than 5 signals, no authentication, 60 req/min rate limit, and 5-min cache. This goes well beyond what annotations provide.

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 long but information-dense, with purpose front-loaded and usage routing at the end. It loses a point for redundancy: 'permanent monthly archive' appears twice in the opening, and the full field list and analyst ID list duplicate structured schema/output schema information.

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?

With an output schema present, field enumeration is not strictly required, yet the description adds valuable interpretation (winRate as 0–1 fraction, avgReturn as % wins only), the <5 signal exclusion, archive retention semantics, coverage of all 10 analysts, and rate/cache limits. An agent has everything needed to invoke this correctly.

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% and the schema already documents analystId, including the allowed IDs and omit-for-all behavior. The description repeats this with the example '?analystId=chain_hawk' but adds no genuinely new parameter semantics, so the baseline of 3 is appropriate.

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 opens with 'Returns the permanent monthly archive of analyst signal performance', naming a specific resource and aggregation level ('one row per analyst per calendar month'). The 'permanent' and 'Never deleted' framing distinguishes this from live or daily-history tools, and the final sentence explicitly routes to those 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?

Provides explicit when-to-use guidance: 'Use this for long-term monthly archive data' and explicit when-not-to-use guidance: 'use the corresponding live or daily-history tool for current or finer-grained data.' This clearly selects between this tool and its siblings without leaving the decision to inference.

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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