regional_stats"Where are [role] jobs concentrated" / "which cities have the most [job]" / "regional job distribution for [category]" — current job counts broken down by region within a country. Use for "where should I move for this job" / "labor market concentration" questions.
| Name | Required | Description | Default |
|---|---|---|---|
| country | Yes | ||
| category | No | ||
| location_filter | No |
| Name | Required | Description | Default |
|---|---|---|---|
| locations | No | Job counts by region |
Changes observed during successful MCP inspections.
Input schema / examplesAdded value: +[
+ {
+ "country": "gb"
+ },
+ {
+ "category": "Engineering Jobs",
+ "country": "fr"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "locations": {
+ "description": "Job counts by region",
+ "items": {
+ "properties": {
+ "location": {
+ "properties": {
+ "display_name": {
+ "description": "Region name",
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "total_postings": {
+ "description": "Job count in region",
+ "type": "number"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds context about returning current job counts by region, which aligns with and enriches the annotation hints without contradiction.
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?
The description is a single well-structured sentence with embedded example queries. It is concise, front-loaded, and contains no extraneous information.
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?
Given the presence of an output schema, the description does not need to explain return values. It covers key usage scenarios and parameters, though it lacks details on error handling or behavior for missing data. Overall, it is complete enough for a read-only statistics tool.
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 0%, so the description must compensate. It explains country and category via examples and context ('within a country', 'regional job distribution for [category]'), but the location_filter parameter is not explained. This partial coverage results in adequate but not complete parameter semantics.
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 explicitly states the tool provides job counts broken down by region within a country, with example queries that clarify its purpose. It distinguishes itself from sibling tools which are focused on other functionalities like research or entities.
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 includes example queries and explicitly states 'Use for' scenarios like labor market concentration questions. While it does not mention alternatives or when not to use, the guidance is clear and contextually sufficient.
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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