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Brand24 MCP Server and API by Cotlet

List AI events in Brand24

brand24_list_ai_events
Read-onlyIdempotent

List AI events in the user's own Brand24 account from ChatGPT, Claude or any AI agent. Returns how to do it in Brand24 step by step (screens, buttons, fields) and opens nothing by itself; then do it with the cotlet_browser_* tools in the user's Brand24 Cloud Browser. Brand24's official API: Yes.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "brand24_list_ai_events"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.5/5.0
Behavior4/5

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

The annotations already establish readOnlyHint, idempotentHint, non-destructive and closed-world, so the safety bar is low. The description still adds genuine value by stating "opens nothing by itself" (no side effects on the account) and disclosing the return shape as step-by-step screens, buttons and fields, which the annotations cannot convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose is front-loaded and no sentence is purely redundant, but the trailing "Brand24's official API: Yes." is unexplained filler that does not help an agent decide anything. The parenthetical "(screens, buttons, fields)" adds useful precision, while the overall sentence runs long and is awkwardly chained.

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 zero-parameter, read-only guide tool with no output schema, the description covers what is returned and who performs the action, which is the essential information. The main remaining gap is the undefined notion of "AI events" and the unexplained API note, but nothing blocks correct invocation.

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 has zero parameters and 100% schema coverage, so parameter semantics carry no burden. The description correctly implies there is no input to supply and focuses on what the call yields instead, matching the baseline for a no-argument tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb and resource ("List AI events in the user's own Brand24 account") and then clarifies the actual behavior: it returns step-by-step instructions rather than performing the listing. However, the term "AI events" is never defined, and the naming implies the tool itself retrieves a list, so an agent must read carefully to learn it is really a guide. Purpose is understandable but muddied by this tension.

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

Usage Guidelines3/5

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

There is usable workflow guidance — call this, then execute the steps with cotlet_browser_* tools in the Brand24 Cloud Browser. What is missing is any when-not guidance or differentiation from sibling guidance tools such as cotlet_app_guide or cotlet_apps. Usage is implied rather than stated as an explicit decision rule.

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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