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flayr

by social.flayr
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Add or remove labels on many items

labels_bulk_edit

[writes flayr data · write scope] Adds and/or removes labels on up to 100 content items in one call: each item becomes (labels − remove) + add. Published items are fine (labels are metadata). Each item succeeds or fails on its own; read results. Removing source:ai-generated does not turn a post's AI label off: with no explicit toggle it follows how the media was made, and the result carries a warning. For real footage also call update_content with aiGenerated: false on each item. (Wraps POST /api/v2/content/labels.)

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": {
      "ids": "<ids>"
    },
    "name": "labels_bulk_edit"
  }
}

Input Schema

NameRequiredDescriptionDefault
addNoLabels to add. Normalized "key:value" labels (keys and values are lower-cased, spaces become "-"). Linked keys: campaign:<media campaign id>, experiment:<experiment id> (+ variant:<name> or the experiment's compareKey), type:<post type> (short, long-form, carousel, image, text, reel, story, poem, timelapse…). Free keys work too: cut:opus, lane:tech, parent:pantheon-part-1, ab:<pair id> (an A/B pair). Labels are copied onto every post made from the draft, and compared in GET /api/v2/analytics/experiments.
idsYesContent ids (GET /api/v2/content).
removeNoLabels to remove. Not also in `add`.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the annotations (write, not idempotent, not destructive), the description discloses the exact mutation formula '(labels − remove) + add', per-item independent success/failure, that a 'results' array must be read, and the important caveat that media-derived AI labels ignore removal and return a 'warning'. The annotations would never tell an agent any of this.

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 scope tag is front-loaded and each sentence carries a distinct fact (formula, publish safety, per-item results, AI-label caveat, cross-tool remedy, endpoint). It is dense but well ordered; the parenthetical wrapper note is minor overhead.

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 3-parameter batch mutation with no output schema, the description covers mutation semantics, partial failure, the 'results' payload to inspect, and a cross-tool follow-up for the AI-label edge case. It could go slightly further on the 'results' entry shape or rate/limit behavior, but nothing needed to call it correctly is missing.

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?

Schema coverage is 100%, so the baseline is 3; the description still adds value by explaining how `add` and `remove` compose per item and by flagging that removal of the AI-generated label is overridden by media provenance. It does not restate label syntax, which is fine since the schema handles that.

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 gives a specific verb pair, the resource ('labels on content items'), and the batch scope ('up to 100 in one call'). It also names the write scope and the underlying endpoint, so an agent can distinguish it from compare_labels (read-only comparison) and update_content 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 Guidelines4/5

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

The description tells the agent when to use it, and when its effect is insufficient: removing `source:ai-generated` does not turn off the AI label, and in that case the agent should call update_content with `aiGenerated: false` per item. It also clarifies that published items are acceptable to edit. It lacks a general 'use this instead of update_content for single items' routing statement, so it is clear but not exhaustive.

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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