list_workspaces
The workspaces this token can see.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Cadencz
TDQS
A3.5/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It does reveal that results are limited to what the current token can see, implying a token-scoped read-only operation, but it does not mention pagination, error behavior, or response structure.
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 concise phrase with no wasted words. It is front-loaded with the essential information: the resource and its scope.
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 zero-parameter, simple list tool with no output schema, this description is largely sufficient for invocation and selection. It lacks explicit return-field details, but that is a minor gap given the tool's simplicity.
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?
The tool accepts no parameters, so there is no parameter documentation burden on the description. The zero-parameter baseline of 4 applies here.
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 identifies the resource (workspaces) and the token-scoped visibility, which distinguishes it from sibling tools like list_brains and list_channels. However, it lacks an explicit verb like 'list' or 'return', so purpose clarity is strong but not perfect.
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?
No guidance is provided about when to use this tool versus alternatives such as list_brains or list_channels. The description gives no context about selection criteria, exclusions, or alternative 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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TDQS
B3.4/5.0
Disambiguation5/5
Each tool targets a distinct action or resource: plan approval, scheduling, publishing, analytics, planning, and listing all have clear boundaries. Even the schedule/publish/approve tools are easy to distinguish because their source objects and outcomes are explicitly different.
Naming Consistency4/5
The naming almost uniformly follows a verb_noun snake_case pattern, such as list_brains, compose_post, and cancel_scheduled_post. The one small deviation is publish_now, which uses an adverb rather than a noun object, but the overall pattern remains highly predictable.
Tool Count5/5
14 tools is well within the ideal range for a content planning and social media scheduling server. Each tool addresses a meaningful part of the workflow without unnecessary bloat or redundant helpers.
Completeness3/5
The tool surface covers planning, composing, scheduling, publishing, canceling, analytics, and workspace/channel context. However, there are notable lifecycle gaps: no update or delete operation for drafts or scheduled posts, and no way to fetch a single post's detail beyond a list.