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aidelly

by io.github.wilzer
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aidelly_list_image_editor_outputs

Read-onlyIdempotent

List the workspace's saved AI-generated and edited image outputs, freshest first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryNoOptional query overrides for endpoints with sparse parameter schemas.
cursorNo
brand_idNo
workspace_idNoWorkspace 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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / cursor
      Added value: +{
+  "maxLength": 512,
+  "type": "string"
+}
  2. Changed1 schema field changed
    • addedInput schema / properties / workspace_id / description
      Added 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."
  3. First observed

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover read-only, idempotent, non-destructive behavior. The description adds useful context: 'freshest first' ordering and workspace scoping. But it does not disclose pagination behavior, return format, or other edge-case behaviors, so it only moderately supplements 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence. It front-loads the action and resource, contains no filler, and is appropriately sized for a simple list operation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 5 parameters and no output schema. The description covers only the scope and ordering. It doesn't address pagination, filter semantics, response shape, or how workspace_id is resolved, leaving gaps for an agent to fully understand the tool's behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema coverage is low (40%), with only query and workspace_id described. The description adds no parameter-specific detail and does not explain limit, cursor, or brand_id, failing to compensate for the undocumented parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists the workspace's saved AI-generated and edited image outputs, with 'freshest first' ordering. This specific verb+resource combination distinguishes it from sibling list tools like list_posts or list_drafts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context: it targets the workspace's image editor outputs, which is unambiguous among many list_* siblings. However, it does not explicitly state when to use this over alternatives or mention any exclusions.

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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