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by com.cryptowhaleinsights
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arbitrage_monthly

Read-onlyIdempotent

Cross-exchange arbitrage permanent monthly archive — Returns the permanent monthly archive of cross-exchange arbitrage opportunities — one row per symbol per calendar month, aggregated from daily snapshots before they are purged after 180 days. This archive is never deleted and grows indefinitely, enabling AI agents to answer historical questions like 'which token consistently had the highest arbitrage spread?' across months of data. Each row includes: month (YYYY-MM-01), symbol, avgSpreadPct (average % spread that cycle), occurrenceCount (how many daily snapshots contributed), buyExchange, sellExchange, avgUsdVolume, daysInMonth. Months with fewer than 5 daily records are excluded. Data source: CryptoWhaleInsights arbitrage scanner (DexScreener allPairs, 158 tokens). No authentication required. 60 req/min. 1-hr 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
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalNo
monthsNo
updatedAtNo
dataSourceNo
attributionNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "attribution": {
+      "$ref": "#/components/schemas/Attribution"
+    },
+    "dataSource": {
+      "type": "string"
+    },
+    "months": {
+      "items": {
+        "properties": {
+          "avgSpreadPct": {
+            "description": "Average % spread between buy and sell exchange that month.",
+            "type": "number"
+          },
+          "avgUsdVolume": {
+            "type": "number"
+          },
+          "buyExchange": {
+            "type": "string"
+          },
+          "daysInMonth": {
+            "type": "integer"
+          },
+          "month": {
+            "description": "First day of the month (YYYY-MM-01, UTC).",
+            "format": "date",
+            "type": "string"
+          },
+          "occurrenceCount": {
+            "description": "Number of daily snapshots where this symbol appeared.",
+            "type": "integer"
+          },
+          "sellExchange": {
+            "type": "string"
+          },
+          "symbol": {
+            "type": "string"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total": {
+      "type": "number"
+    },
+    "updatedAt": {
+      "format": "date-time",
+      "type": "string"
+    }
+  },
+  "type": "object"
+}
  2. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior, and the description adds substantial behavioral context beyond that: data is aggregated from daily snapshots before purging, the archive is never deleted, it grows indefinitely, and records are excluded if they have fewer than 5 daily contributions. It also discloses the data source, authentication requirements, rate limit, and cache behavior.

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 dense and well-organized, front-loading the core purpose before adding details about aggregation, exclusion rules, source, and usage guidance. Minor redundancy exists in repeating 'cross-exchange arbitrage permanent monthly archive' twice at the start, but every other sentence earns its place.

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?

The definition is complete for a parameterless tool: it describes the data granularity, listed output fields, exclusion rules, data source, authentication needs, rate limits, cache behavior, and explicit comparison to sibling tools. The presence of an output schema also relieves the description from needing to detail return structure.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero parameters, so the baseline is 4. The description adds no parameter-level detail because none is needed; it instead describes the fixed output shape and filtering rules, which is appropriate for a parameterless tool.

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 explicitly states it 'Returns the permanent monthly archive of cross-exchange arbitrage opportunities' with one row per symbol per calendar month. It precisely differentiates itself from sibling tools by specifying monthly aggregation and permanent retention, making the purpose unmistakable.

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?

It explicitly instructs when to use this tool: 'Use this for long-term monthly archive data; use the corresponding live or daily-history tool for current or finer-grained data.' It further clarifies data exclusions, like months with fewer than 5 daily records, and provides practical constraints such as rate limits and cache duration.

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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