batch_predictPredict nationalities for multiple first names at once (up to 10). Returns country codes with probability scores for each name. Use to process name lists efficiently.
| Name | Required | Description | Default |
|---|---|---|---|
| names | Yes | Array of first names to predict nationality for (maximum 10). |
| Name | Required | Description | Default |
|---|---|---|---|
| results | Yes | Array of nationality predictions for each input name |
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "results": {
+ "description": "Array of nationality predictions for each input name",
+ "items": {
+ "properties": {
+ "name": {
+ "description": "The input first name",
+ "type": "string"
+ },
+ "nationalities": {
+ "description": "List of predicted nationalities ranked by probability",
+ "items": {
+ "properties": {
+ "country_code": {
+ "description": "ISO country code",
+ "type": "string"
+ },
+ "probability": {
+ "description": "Probability score between 0.0 and 1.0",
+ "type": "number"
+ }
+ },
+ "required": [
+ "country_code",
+ "probability"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "sample_size": {
+ "description": "Sample size from nationalize.io dataset",
+ "type": "number"
+ }
+ },
+ "required": [
+ "name",
+ "sample_size",
+ "nationalities"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "results"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "names": [
+ "Anna",
+ "Giuseppe",
+ "Fatima",
+ "James"
+ ]
+ },
+ {
+ "names": [
+ "Jean",
+ "Sofia",
+ "Ahmed",
+ "Kenji",
+ "Lars"
+ ]
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly and idempotent hints, so the description doesn't need to repeat safety. It adds valuable context beyond annotations: the return format ('country codes with probability scores') and the batch limit ('up to 10'), which help set expectations for the agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero filler. The description front-loads the core purpose and adds the most important limitation (up to 10) and return format. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With a single parameter, full schema coverage, a rich output schema, and annotations covering safety, the description provides enough context for the agent to invoke the tool correctly. It explains what the tool does, how to use it, and what the result contains.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter already described as 'Array of first names to predict nationality for (maximum 10).' The description essentially repeats this information without adding new meaning, so it stays at the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Predict') and clearly specifies the resource ('nationalities for multiple first names'). It explicitly distinguishes itself from the sibling tool predict_nationality by highlighting batch processing ('at once', 'up to 10').
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states a clear use case: 'Use to process name lists efficiently.' This implies when to use it (multiple names) but does not explicitly mention alternatives or when not to use it. It falls short of a 5 because it lacks an explicit contrast with predict_nationality for single names.
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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