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ai-content-drop

by com.aicontentdrop
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list_models

Read-onlyIdempotent

See which AI video or image models can make a video ad, animate a photo, produce a product clip or generate an image, with the credit cost of one generation. No account needed. The list is live; count is the number of models currently offered.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoModel family to list. Defaults to video.
max_creditsNoOnly return models costing this many credits or fewer.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNo
typeYes
countYesModels returned after filtering.
modelsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedOutput schema / properties / models / items / properties / credits / description
      Previous value: -"Flat cost per generation."New value: +"Cost of one generation at the model's default settings; estimate_credit_cost quotes other lengths."
    • addedOutput schema / properties / models / items / properties / needs_start_image
      Added value: +{
+  "description": "True when the model only animates an image: pass image_url to generate_video.",
+  "type": "boolean"
+}
  2. Changed1 schema field changed
    • addedInput schema / required
      Added value: +[]
  3. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already establish readOnly/idempotent/non-destructive, but the description adds genuinely new behavioral facts: no authentication is required and the list is live (so results change over time). It also clarifies that `count` reflects models currently offered. No safety or rate-limit info, but for a read-only catalog this is solid added value.

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?

Three tight sentences: capability scope first, then the no-account and live-list facts, then the meaning of `count`. No filler and the most decision-relevant information leads.

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?

With 0 required params, 100% schema coverage, and an output schema present, the description need not restate return fields; it still goes further by defining `count` and the live nature of the list. Nothing an agent needs to call this correctly is missing.

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 coverage is 100%, so both parameters (type enum, max_credits threshold) are already fully documented in the schema — the baseline of 3 applies. The description's purpose phrases ('make a video ad, animate a photo, ... generate an image') loosely map to the video/image families but add no syntax or edge-case detail beyond the schema.

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?

Specific verb (see/list) plus resource (AI video or image models) and concrete scope: what each model can produce and its credit cost for one generation. An agent can distinguish it from generate_video, generate_image, or estimate_credit_cost without opening any schema.

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?

Clear context for use: it is a live catalog of available models, needs no account, and reports per-generation credit cost — useful before choosing a generator. It stops short of explicitly naming sibling alternatives (e.g., if you already know the model, go straight to generate_video), so usage is strong but not fully routed.

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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