XCut AI – Viral Content Research

Import a profile's recent posts

xcut_import_profile

Use this when the user wants to study a creator's or brand's recent posts in depth (Instagram, TikTok, YouTube or Facebook): it adds them to a canvas as a group and transcribes and analyses each. About 1-15 credits plus transcription. Returns a job_id while the profile is read. Do not use just to rank posts (use xcut_find_outliers).

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": {
      "profile": "<profile>",
      "platform": "<platform>"
    },
    "name": "xcut_import_profile"
  }
}

Input Schema

Name	Required	Description
`posts`	No	How many recent posts (default 6).
`profile`	Yes	Profile link or @handle.
`platform`	Yes	instagram, tiktok, youtube or facebook.
`canvas_id`	No	Optional. Leave it out unless the user names a canvas: XcutAI then uses their "From AI apps" canvas. Ids come from xcut_list_canvases.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 9, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already flag readOnlyHint=false, idempotentHint=false and openWorldHint=true, so the write/external nature is covered. The description adds real value on top: cost ('About 1-15 credits plus transcription') and async behavior ('Returns a job_id while the profile is read'). It stops short of saying how to poll the job (xcut_get_job) or what the analysis output contains.

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 triggering condition, and every clause earns its place — scope, platforms, cost, return value, and the exclusion all arrive in the first pass.

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

Completeness4/5

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

For a 4-param async mutation with no output schema, the description covers trigger, platforms, cost, side effects, and the key return (job_id). The one omission is the follow-up step for retrieving results once the job completes, which the job_id implies but does not state.

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 profile, platform, posts, and canvas_id are all documented in the schema, including the canvas_id default behavior. The description adds only the implicit canvas-placement effect and the credit cost, not syntax or format details beyond the schema. Baseline 3 is appropriate.

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+resource (import a profile's recent posts), the four supported platforms, and the side effects (adds them to a canvas as a group, transcribes and analyses each). It also names the sibling it is not (xcut_find_outliers), so an agent can distinguish it without opening schemas.

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?

Explicitly gives the when ('user wants to study a creator's or brand's recent posts in depth') and the when-not with a named alternative ('Do not use just to rank posts (use xcut_find_outliers)'). Nothing is left to inference.

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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