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PostKing MCP Server

PostKing MCP Server

by bitsandtea
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

humanize_text

Reduce AI detection in text using LLM rewrite and BERT replacements to produce natural, human-like content.

Instructions

Apply LLM rewrite and BERT replacements to reduce AI detection signals in text.

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": "humanize_text"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesText to humanize
brandIdNoBrand ID (uses active brand if omitted)
platformNoPlatform context: x | linkedin | instagram | threads | facebook

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.7

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description must carry the burden of disclosing side effects, return behavior, and prerequisites. It only mentions the method and outcome, omitting whether the input is mutated, what the response contains, or if a brand context is required. This is insufficient for safe invocation.

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 a single, concise sentence that front-loads the action and purpose. Every word contributes value, with no redundancy or extraneous detail.

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

Completeness2/5

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

Given no annotations and no output schema, the description is too sparse to fully inform an agent. It lacks context about when to select this tool over similar siblings, what to expect as a response, or any operational constraints, making it incomplete for reliable use.

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 provides descriptions for all three parameters (text, brandId, platform), achieving 100% coverage. The tool description adds no extra meaning about how these parameters affect the output, 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.

Purpose4/5

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

The description states a specific action ('Apply LLM rewrite and BERT replacements') and a clear outcome ('reduce AI detection signals in text'). It distinguishes from generic rewriting by mentioning AI detection, but does not explicitly contrast with sibling tools like rewrite_text.

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 (e.g., rewrite_text, rewrite_with_voice). It implies usage 'when you want to reduce AI detection' but lacks explicit scenarios or exclusions.

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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