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isGenAI

by com.isgenai
OverviewDiscussionsAdmin

How many replies under this X post are AI or bots?

estimate_x_ai_replies
Read-onlyIdempotent

Estimate what share of the replies under an X (Twitter) post look AI-generated or bot-driven. Pass the reply texts with any author details (handle, bio, followers, following, joined date, default avatar) and timestamps you have; the post text and time sharpen the estimate. Returns the percentage, a band and a 0-100 score with signals for every reply. An estimate from patterns, not proof about any account. Docs: https://isgenai.com/x-ai-replies

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": {
      "replies": "<replies>"
    },
    "name": "estimate_x_ai_replies"
  }
}

Input Schema

NameRequiredDescriptionDefault
postNo
repliesYesThe replies to score, one object per reply.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive behavior, and the description adds important context: this is an estimate from patterns, not proof about any account. It also explains the return shape, including percentage, band, 0-100 score, and per-reply signals, though it omits operational limits such as the 500-reply maximum.

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?

The description is front-loaded with the purpose, then input guidance, return values, and the key caveat. Every sentence contributes useful information, and the docs link is appended rather than disrupting the main flow.

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?

Given the nested input schema, lack of output schema, and the need to explain that this is a probabilistic estimate, the description is largely complete. It explains the return values and caveat, but it does not mention the 500-reply cap or give more detail on what the per-reply signals represent.

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 description adds real meaning beyond the schema by naming the author details that help the estimate, including handle, bio, followers, following, joined date, and default avatar, and by saying the post text and time sharpen results. With only 50% schema description coverage, this compensates well, though it does not cover every nested field such as verified, likes, or id.

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 and resource: estimate what share of replies under an X post look AI-generated or bot-driven. The scope is precise enough to distinguish this from generic sibling checks like check_text or check_url, even without naming them.

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?

It clearly establishes the usage context: pass reply texts under an X post, with optional author details, timestamps, and post text to sharpen the estimate. It does not explicitly say when to prefer this over check_text or check_url, and it gives no exclusions, so it falls short of the full when/when-not/alternatives standard.

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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