ai-content-drop

List boards

list_canvases

Read-onlyIdempotent

List the account's boards (visual canvases), newest first: id, name, revision, card count and the board's page link. Costs no credits. Its ids are the canvas_id the other board tools take; an empty list means the account has no board yet. 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
No arguments

Output Schema

Name	Required	Description	Default
`count`	Yes
`canvases`	Yes
`next_step`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedOct 8, 2026

removedOutput schema / properties / canvases / items / properties / updated_at
```
Removed value: -{
-  "type": [
-    "string",
-    "null"
-  ]
-}
```

changedOutput schema / properties / canvases / items / required

Previous value: -[
-  "id",
-  "name",
-  "revision",
-  "card_count",
-  "updated_at",
-  "link"
-]New value: +[
+  "id",
+  "name",
+  "revision",
+  "card_count",
+  "link"
+]

AddedSep 11, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description adds genuinely new behavior: zero credit cost, first-class auth requirements (OAuth prompt or Bearer acd_live_… API key with a URL), and the meaning of an empty result. These are exactly the operational facts an agent needs before calling.

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?

Front-loaded with the purpose and return fields, then cost, id linkage and auth. The auth sentence is long and dense with an embedded URL, but it is the only verbose part and carries real information for a first-call tool.

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?

Output schema exists so return values needn't be re-specified, annotations carry the safety profile, and the description covers cost, auth, empty-state and id usage. Nothing material is missing for a zero-parameter list tool.

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 takes zero parameters, so the schema cannot mislead and the baseline is 4. The description correctly implies no filtering arguments are needed while still describing what the caller receives.

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?

States a specific verb and resource ('List the account's boards (visual canvases)') plus ordering ('newest first') and the exact fields returned. It also distinguishes itself from get_canvas by explaining that the ids it returns are the canvas_id consumed by the other board 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?

Clearly frames this as the discovery step whose ids feed the other board tools, and explains the empty-list case ('an empty list means the account has no board yet'). It does not explicitly say when to prefer get_canvas over this or address pagination limits, so it stops short of full when/when-not guidance.

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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