ExpenseBot

get_deep_analytics

Read-only

Run deeper, multi-step analytics on the user's expenses. Use for explanatory questions like 'why did my spending increase' or 'compare Q1 vs Q2'. Takes 10-30 seconds (runs as a background job, polled automatically). Returns: { message, data: { ..., sampleMeta? } } where sampleMeta.isTruncated indicates whether the agent saw the full dataset.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`query`	Yes	The analytics question to answer
`dateRange`	No	Time period filter. Use exactly one variant — pick the shape that matches the user's phrasing.

Output Schema

TableJSON Schema

Name	Required	Description
`data`	No	Inline branch: computed analytics, or null when no spreadsheet / no data.
`type`	No	Async branch only: "analytics_pending" — poll pollEndpoint or call poll_analytics with jobId.
`jobId`	No	Async branch only.
`message`	No	Human-readable narrative (both branches).
`success`	No	Async branch only.
`response`	No	Chat alias of message.
`pollEndpoint`	No	Async branch only.
`pollInterval`	No	Async branch only: milliseconds.
`estimatedTime`	No	Async branch only: seconds.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 9, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "description": "Two branches. Async: type='analytics_pending' with jobId — poll poll_analytics until complete. Inline: message + data with the computed analytics (data may be null when there is no spreadsheet or no data yet). success is present only on the async branch.",
+  "properties": {
+    "data": {
+      "additionalProperties": true,
+      "description": "Inline branch: computed analytics, or null when no spreadsheet / no data.",
+      "type": [
+        "object",
+        "null"
+      ]
+    },
+    "estimatedTime": {
+      "description": "Async branch only: seconds.",
+      "type": "number"
+    },
+    "jobId": {
+      "description": "Async branch only.",
+      "type": "string"
+    },
+    "message": {
+      "description": "Human-readable narrative (both branches).",
+      "type": "string"
+    },
+    "pollEndpoint": {
+      "description": "Async branch only.",
+      "type": "string"
+    },
+    "pollInterval": {
+      "description": "Async branch only: milliseconds.",
+      "type": "number"
+    },
+    "response": {
+      "description": "Chat alias of message.",
+      "type": "string"
+    },
+    "success": {
+      "description": "Async branch only.",
+      "type": "boolean"
+    },
+    "type": {
+      "description": "Async branch only: \"analytics_pending\" — poll pollEndpoint or call poll_analytics with jobId.",
+      "type": "string"
+    }
+  },
+  "required": [],
+  "type": "object"
+}

Changed1 schema field changedAug 25, 2026

changedOutput schema / (root)

Previous value: -{
-  "additionalProperties": true,
-  "description": "Standard ExpenseBot tool result envelope. `message` is the human-readable summary the AI cites; `data` is the structured payload (totals, breakdowns, ids, etc.). On failure, `success` is false and `error` carries a code/message/hint triple.",
-  "properties": {
-    "data": {
-      "additionalProperties": true,
-      "description": "Structured payload. Shape varies per tool — common keys: total, breakdown, comparison, sampleMeta, ids, expenseId, reportId, signupUrl, results.",
-      "type": "object"
-    },
-    "error": {
-      "additionalProperties": true,
-      "description": "Present only when success === false.",
-      "properties": {
-        "code": {
-          "type": "string"
-        },
-        "hint": {
-          "type": "string"
-        },
-        "message": {
-          "type": "string"
-        }
-      },
-      "type": "object"
-    },
-    "message": {
-      "description": "Human-readable result text. Always present on success; prefer rendering this verbatim before any further reasoning.",
-      "type": "string"
-    },
-    "sampleMeta": {
-      "additionalProperties": true,
-      "description": "Set when the underlying dataset was truncated. isTruncated=true means the agent saw a sample of `sampleCount` of `totalCount` rows; aggregate totals are still accurate.",
-      "properties": {
-        "isTruncated": {
-          "type": "boolean"
-        },
-        "sampleCount": {
-          "type": "integer"
-        },
-        "totalCount": {
-          "type": "integer"
-        }
-      },
-      "type": "object"
-    },
-    "success": {
-      "description": "False on tool errors; check before reading `data`.",
-      "type": "boolean"
-    }
-  },
-  "type": "object"
-}New value: +null

First observedAug 10, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description enriches this with important operational behavior: it runs as a background job, takes 10-30 seconds, is polled automatically, and may return truncated data via sampleMeta.isTruncated. This goes beyond the structured annotations and gives the agent accurate expectations for latency and result completeness.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is three sentences with no fluff: purpose, example use cases, latency/background behavior, and return shape are each packed into distinct, front-loaded statements. Every sentence earns its place and the key operational constraint (10-30 seconds) appears early.

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 complex analytics tool, the description covers purpose, invocation context, asynchronous behavior, and important return metadata (sampleMeta.isTruncated). The schema and output schema handle the parameter and return details, so nothing critical is missing for an agent to call 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%, so the schema already documents both `query` and the `dateRange` variants in detail. The description reinforces the query semantics with examples but does not add substantial new parameter meaning beyond what the schema provides. This is the appropriate baseline.

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 states a specific verb and resource ('Run deeper, multi-step analytics on the user's expenses') and gives concrete example questions that distinguish it from simpler reporting tools like get_spending_summary or get_pnl. The 'deeper, multi-step' phrasing and explanatory-question use cases make the tool's purpose clear and differentiated.

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 explicitly says 'Use for explanatory questions' and provides examples, giving clear guidance on when to invoke this tool. It does not explicitly name alternatives or state when not to use it, so it stops short of a full when-to-use vs. when-not-to-use matrix, but the context is unambiguous enough for an agent to route correctly.

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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