PostKing MCP Server

check_ai_content

Identify AI-generated content. Submit text to get a probability score and analysis, enabling you to verify authenticity and origin.

Instructions

Check whether text is likely AI-generated. Returns a score and analysis.

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": {
      "text": "<text>"
    },
    "name": "check_ai_content"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes	Text to check
`brandId`	No	Brand ID (uses active brand if omitted)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.1.7Aug 4, 2026

TDQS

A3.6/5.0

Behavior3/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It does disclose that the tool returns a score and analysis, implying a non-mutating check. However, it does not elaborate on interpretation of the score, limitations, or any side effects, leaving some behavioral aspects unclear.

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 exceptionally concise, consisting of two short sentences that deliver essential information without any fluff. Every word earns its place, and the purpose and return value are immediately clear.

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 relatively simple detection tool, the description adequately covers what the tool does and what it returns. The absence of an output schema is partially mitigated by the mention of 'score and analysis,' though a bit more detail on the output structure could improve completeness. Overall, it is sufficient given the tool's simplicity.

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?

The input schema already provides descriptions for both parameters ('Text to check' and 'Brand ID (uses active brand if omitted)'), achieving 100% schema description coverage. The tool description does not add further parameter-level semantics beyond the structured schema, so the baseline score of 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?

The description clearly states the tool's purpose with a specific verb ('Check'), resource ('text'), and outcome ('whether text is likely AI-generated'). It also states it returns a score and analysis, making it distinct from siblings like humanize_text which performs the opposite transformation.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives, nor any exclusions or context. It is simply a standalone statement of functionality without indicating scenarios or relationships to other tools.

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

Deploy Server