XCut AI – Viral Content Research

Get a canvas

xcut_get_canvas

Read-onlyIdempotent

Use this when the user asks what is on one of their XcutAI canvases: its items (imported posts, notes, chats, groups, Meta ads, analytics tables, websites, media) with ids, counts by type and short previews. Filter with item_type; paginate with offset. Free. Do not use to read an item in full (use xcut_get_content).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "canvas_id": "<canvas_id>"
    },
    "name": "xcut_get_canvas"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Most items to return, 1-100. Default 50.
`offset`	No	Items to skip, for paging through a large canvas. Default 0.
`canvas_id`	Yes	Canvas id (a UUID) from xcut_list_canvases.
`item_type`	No	Only list this kind of item.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 9, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already cover read-only/idempotent/non-destructive, so the description's added value is 'Free' (cost) and the shape of the payload (ids, counts, previews), which is genuinely useful given there is no output schema. It stops short of noting what a canvas with zero items returns or any rate/limit caveats.

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 sentences, front-loaded with the trigger condition, followed by the return contents and the disambiguation. No filler and nothing buried.

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 no output schema, the description compensates by describing the return payload (items, ids, counts by type, short previews) and the filtering/paging model. Combined with complete schema coverage and full annotations, an agent has everything needed to invoke it correctly.

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 every parameter (limit, offset, canvas_id, item_type) is already documented in the schema. The description only restates the item_type filter and offset paging, adding no syntax or semantics beyond it — the correct baseline of 3.

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 ('get') and resource ('canvas') and enumerates exactly what is returned: items with ids, counts by type, and short previews. It explicitly distinguishes itself from xcut_get_content, which reads a single item in full.

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

Usage Guidelines5/5

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

Names the triggering user intent ('asks what is on one of their XcutAI canvases') and gives an explicit exclusion with the alternative tool to use instead ('Do not use to read an item in full (use xcut_get_content)').

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

All XCut AI – Viral Content Research tools Inspect

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