parse_dashboard_workspaceList the selected workspace's brands, plans and markets. Use it to find a brand's slug when the workspace has more than one brand; the job tools (parse_next_tasks, parse_what_changed) default to the workspace's own brand.
| Name | Required | Description | Default |
|---|---|---|---|
| offset | No | ||
| includePlan | No | ||
| includeConnections | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Input schema / $schemaAdded value: +"https://json-schema.org/draft/2020-12/schema"Input schema / properties / includeConnectionsAdded value: +{
+ "default": false,
+ "type": "boolean"
+}Input schema / properties / includePlanAdded value: +{
+ "default": false,
+ "type": "boolean"
+}Input schema / properties / offsetAdded value: +{
+ "default": 0,
+ "maximum": 10000,
+ "minimum": 0,
+ "type": "integer"
+}Output schema / (root)Previous value: -nullNew value: +{
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, non-destructive, and closed-world behavior, so the description only needs to add context. It adds selected-workspace scope and default-brand behavior for job tools, but it does not explain pagination or the optional include flags.
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 tightly written sentences with zero waste. The scoping and usage guidance are front-loaded and easy to scan.
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?
For a simple list tool with annotations and an output schema, the description covers purpose and usage adequately. However, leaving all three optional parameters undocumented is a clear gap for callers who need to control plan or connection inclusion.
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?
The schema has 0% description coverage, and all three optional parameters (offset, includePlan, includeConnections) are undocumented in both schema and description. The mention of plans and markets hints at returned data but does not clarify parameter meanings or defaults.
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 states a specific verb, 'List,' and specific resources: the selected workspace's brands, plans, and markets. It also distinguishes the tool's role from job tools by noting their default brand behavior.
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?
It explicitly says to use this tool to find a brand's slug when the workspace has more than one brand, and it notes that job tools default to the workspace's own brand. It stops short of stating when not to use it or naming other sibling alternatives.
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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