GET /content-automations
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Optional query overrides for endpoints with sparse parameter schemas. | |
| cursor | No | ||
| brand_id | No | ||
| workspace_id | No | Workspace to operate in. Do not ask the user for this UUID — call aidelly_list_workspaces and use the `id` of the matching workspace. Optional for read operations; required when creating content. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / cursorAdded value: +{
+ "maxLength": 512,
+ "type": "string"
+}Input schema / properties / workspace_id / descriptionAdded value: +"Workspace to operate in. Do not ask the user for this UUID — call aidelly_list_workspaces and use the `id` of the matching workspace. Optional for read operations; required when creating content."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is known. However, the description adds no additional behavioral context (e.g., what is returned, pagination behavior, or how cursors affect results). It merely repeats the HTTP method, which adds no value beyond the annotations.
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 only a single short string, which is concise, but it is under-specification rather than effective conciseness. It does not earn its place because it conveys no actionable information about the tool's purpose or behavior.
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?
This tool has 4 optional parameters, no output schema, and numerous siblings, yet the description provides no information about return values, usage scenarios, or relationship to related tools. It is completely inadequate for an agent to select and invoke this tool correctly.
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 50% (only query and workspace_id have descriptions). The description itself provides no information about parameters like cursor or brand_id, failing to compensate for the missing schema documentation. It adds no meaning beyond the raw parameter names.
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 'GET /content-automations' essentially restates the tool name as an HTTP path. It does not explicitly state that it retrieves or lists content automations, nor does it provide any meaningful description of the tool's function beyond the route. This is a tautology of the name 'get_content_automations'.
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?
There is no guidance on when to use this tool versus alternatives like get_content_automations_id or post_content_automations. The description provides no context about use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools have overlapping purposes, especially in approvals and post updates. For example, aidelly_action_post_approval and aidelly_post_approvals_id_action both handle approval actions, while aidelly_get_approvals and aidelly_list_pending_approvals appear to serve similar listing functions. The proliferation of HTTP-route-style names with generic prefixes (get_approvals_id, patch_posts_id) further obscures distinctions.
The tool set mixes clean verb_noun names (create_post, list_posts) with HTTP-method-plus-path names (get_approvals_id, patch_posts_id, post_analytics_insights). There is no consistent pattern, and 'post' as a verb collides with the content post noun, creating confusion. The inconsistent conventions make it hard to predict tool names.
With 124 tools, this server vastly exceeds the typical MCP server scope and the calibration threshold (50+ is extreme). Even for a comprehensive platform API, such a large surface overwhelms agents and makes selection impractical. The count is a severe mismatch for a usable MCP tool set.
The tool surface is remarkably comprehensive, covering CRUD for posts, drafts, scheduled posts, tasks, reports, webhooks, workspaces, e-commerce stores, and many more. Advanced features like approvals, repurpose jobs, and content automations are also represented. Minor gaps exist (e.g., no explicit delete for regular posts, only cancel), but the domain is well covered overall.