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whatif_client

Read-only

What if a client pays late or leaves? mode='late30' shifts that client's OPEN invoice amounts out of the near-term expectation (they still owe it, it's just not landing this month). mode='gone' removes that client's trailing monthly income contribution and recomputes Safe Draw against the reduced baseline. Use for questions like 'what if Acme Corp pays 30 days late' or 'what happens if I lose my biggest client'. Client identity is matched against the Income tab's tag/source/description fields — best effort, not a guaranteed match.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes'late30' = shift open invoices 30 days late; 'gone' = client stops paying entirely
clientNameYesThe client's name as it appears on invoices or income rows

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNoThe full what-if scenario: assumptions, evidence, and computed outcome.
messageNoScenario narrative; may be absent if the scenario carried no template or evidence.
successYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "properties": {
+    "data": {
+      "additionalProperties": true,
+      "description": "The full what-if scenario: assumptions, evidence, and computed outcome.",
+      "type": "object"
+    },
+    "message": {
+      "description": "Scenario narrative; may be absent if the scenario carried no template or evidence.",
+      "type": "string"
+    },
+    "success": {
+      "type": "boolean"
+    }
+  },
+  "required": [
+    "success"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • 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
  3. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Despite readOnlyHint=true and destructiveHint=false, the description adds valuable behavioral context: 'late30' shifts open invoices without removing the debt, while 'gone' removes trailing monthly income and recomputes Safe Draw. The best-effort matching caveat against Income tab fields is an important disclosure beyond the annotations.

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?

Three sentences deliver the entire picture: purpose, mode semantics, usage examples, and caveat. It is front-loaded with the 'what if' framing and has little wasted prose—just slightly redundant with the opening question and the example phrasing.

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?

For a two-parameter, read-only what-if tool with an output schema, the description covers the core effect, usage scenarios, and matching caveat. It doesn't explicitly describe the return shape, but with an output schema present that is not required. Minor omission: it doesn't explain what 'Safe Draw' means, but it's a domain term utilized for context.

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?

Schema coverage is 100%, so baseline is 3, but the description enriches both parameters. It clarifies the real-world meaning of 'late30' and 'gone' and adds that clientName matching is best-effort, which is not captured in the schema's field description.

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 scenario ('client pays late or leaves') and explains the effect of each mode, making the tool's purpose unambiguous. The two example questions ('what if Acme Corp pays 30 days late', 'what happens if I lose my biggest client') clearly distinguish this tool from the sibling whatif_afford and whatif_tax_setaside tools.

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 to use it for questions like 'what if a client pays 30 days late' or 'what if I lose my biggest client', giving the agent a clear when-to-use signal. It doesn't name alternatives or state when not to use it, but the scenario-based guidance is strong enough 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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