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by io.github.switchwize
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Card Reality Score

get_reality_score
Read-onlyIdempotent

Return a premium credit-card Reality Score for a card and consumer segment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cardYesCard slug or name, e.g. platinum-card or Chase Sapphire Reserve.
segmentNoOptional segment id, e.g. occasional-traveler, small-business-owner, balance-carrier.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
asOfNoISO timestamp of the underlying data this response reflects.
toolYes
errorNo
resultNo
segmentNo
index_idNo
datasetUrlNo
attributionNo
generatedAtYesISO timestamp this response was computed at.
verified_atNo
schemaVersionYes
methodologyUrlNo
freshnessStatusNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedOutput schema / properties / error / properties / retryable
      Added value: +{
+  "type": "boolean"
+}
  2. Changed2 schema fields changed
    • addedOutput schema / properties
      Added value: +{
+  "asOf": {
+    "description": "ISO timestamp of the underlying data this response reflects.",
+    "type": [
+      "string",
+      "null"
+    ]
+  },
+  "attribution": {
+    "additionalProperties": true,
+    "type": "object"
+  },
+  "datasetUrl": {
+    "type": "string"
+  },
+  "error": {
+    "additionalProperties": false,
+    "properties": {
+      "code": {
+        "type": "string"
+      },
+      "message": {
+        "type": "string"
+      },
+      "suggestions": {
+        "items": {
+          "additionalProperties": true,
+          "type": "object"
+        },
+        "type": "array"
+      }
+    },
+    "required": [
+      "code",
+      "message"
+    ],
+    "type": "object"
+  },
+  "freshnessStatus": {
+    "enum": [
+      "fresh",
+      "aging",
+      "stale",
+      "unavailable"
+    ],
+    "type": "string"
+  },
+  "generatedAt": {
+    "description": "ISO timestamp this response was computed at.",
+    "type": "string"
+  },
+  "index_id": {
+    "type": "string"
+  },
+  "methodologyUrl": {
+    "type": "string"
+  },
+  "result": {
+    "additionalProperties": true,
+    "type": [
+      "object",
+      "null"
+    ]
+  },
+  "schemaVersion": {
+    "type": "string"
+  },
+  "segment": {
+    "additionalProperties": true,
+    "type": "object"
+  },
+  "tool": {
+    "type": "string"
+  },
+  "verified_at": {
+    "type": [
+      "string",
+      "null"
+    ]
+  }
+}
    • addedOutput schema / required
      Added value: +[
+  "schemaVersion",
+  "tool",
+  "generatedAt"
+]
  3. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "type": "object"
+}
  4. First observed

TDQS

B3.2/5.0
Behavior2/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the agent knows the tool is safe and side-effect free. The description adds no behavioral context beyond that—no mention of limitations, data sources, or any special considerations. Since the description carries a lower burden due to annotations, this is adequate but minimal.

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 a single, efficient sentence that front-loads the core purpose and includes the key input dimensions. There is no fluff, redundancy, or unnecessary detail. It earns its place with zero wasted words.

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?

Given the presence of an output schema, the return format is already defined, so the description does not need to explain it. The tool is simple (2 parameters, 1 required) and the annotations cover the safety profile. The description provides the essential purpose and inputs, which is sufficient for an agent to understand what the tool does. It lacks richer context about the nature of the score, but the output schema likely fills that gap.

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 input schema provides complete descriptions for both parameters (card and segment) with examples, so schema coverage is 100%. The description only restates the parameter names in prose ('card and consumer segment') without adding new semantic detail, such as expected formats, value ranges, or relationships between parameters. This meets the baseline for high coverage but adds no extra value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb ('Return') and resource ('premium credit-card Reality Score') with the input scope ('for a card and consumer segment'). It is not a tautology and effectively conveys what the tool does. However, it does not differentiate it from siblings like get_reward_valuations or get_card_offers, so it stops short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus the sibling tools. The description simply states what it does without mentioning alternatives or conditions that would lead an agent to pick this over others. An agent is left to infer from the name and output schema, which is insufficient for a set of closely related financial tools.

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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