ai-content-drop

Get generation

get_generation

Read-onlyIdempotent

Check whether a video or image is finished and get its URL. Takes the job id that generate_video or generate_image returned; pass kind "image" for an image job. Reports status, the finished file URL, and the credits charged. SIGN-IN REQUIRED: connect this server with OAuth (the host prompts for it), or add an API key header "Authorization: Bearer acd_live_…" created at https://aicontentdrop.com/settings/integrations.

Input Schema

Name	Required	Description	Default
`id`	Yes	The id returned by generate_video or generate_image.
`kind`	No	Which job type the id belongs to. Defaults to video.

Output Schema

Name	Required	Description
`id`	Yes	Poll this with get_generation.
`kind`	No
`model`	No	Model ID, underscored.
`title`	No
`status`	Yes	generating \| processing \| completed \| failed \| timeout
`image_url`	No
`video_url`	No
`credits_used`	Yes	Charged only on success — a failed generation is 0.
`error_message`	No
`thumbnail_url`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 8, 2026
- removedOutput schema / properties / created_at
```
Removed value: -{
-  "type": [
-    "string",
-    "null"
-  ]
-}
```

Changed4 schema fields changedSep 9, 2026

changedInput schema / properties / id / description

Previous value: -"The id returned by generate_video."New value: +"The id returned by generate_video or generate_image."

addedInput schema / properties / kind

Added value: +{
+  "description": "Which job type the id belongs to. Defaults to video.",
+  "enum": [
+    "video",
+    "image"
+  ],
+  "type": "string"
+}

addedOutput schema / properties / image_url

Added value: +{
+  "type": [
+    "string",
+    "null"
+  ]
+}

addedOutput schema / properties / kind

Added value: +{
+  "enum": [
+    "video",
+    "image"
+  ],
+  "type": "string"
+}

First observedSep 1, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond annotations: OAuth/API-key authentication requirements and the exact reported fields (status, URL, credits charged).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description front-loads the purpose, then explains the job id, output fields, and auth requirements without excessive detail. The authentication note is somewhat long but earns its place because it is operationally important.

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

Completeness5/5

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

Given the rich annotations, 100% schema coverage, and an output schema, the description is complete enough for an agent to invoke the tool correctly. It covers purpose, input source, output contents, and sign-in requirements.

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

Parameters3/5

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

Schema description coverage is 100%, so both parameters are already documented. The description reinforces that id comes from generate_video/generate_image and that kind selects image jobs, but it adds little beyond the schema itself.

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 states a specific verb and resource: checking whether a video or image generation is finished and retrieving its URL. It also names the sibling generators whose job IDs it consumes, which helps distinguish it from list_generations and the generate_* tools.

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?

It clearly says to use the job id returned by generate_video or generate_image and to pass kind "image" for image jobs. It does not explicitly contrast with list_generations, but the usage context is otherwise clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All ai-content-drop tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.