TrueVoice MCP

Check for AI Slop

check_for_slop

Analyze text for AI slop across information utility, style, and structure, returning specific patterns to avoid for clearer writing.

Instructions

Analyze text for AI slop indicators across three categories: Information Utility, Style Quality, and Structure. Returns specific patterns to avoid.

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_for_slop"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes	The text to analyze for AI slop indicators

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 25, 2026

TDQS

A3.5/5.0

Behavior3/5

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

Annotations are absent, so the description carries the full burden of behavioral disclosure. It states that it analyzes text and returns patterns, which is basic but does not mention any side effects, prerequisites, error conditions, or performance characteristics. For a read-only analysis tool this is adequate, but not comprehensive.

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?

Two sentences with no redundancies. The core purpose and categories are front-loaded, and the return value is clarified in the second sentence. Every word earns its place.

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

Completeness3/5

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

For a simple one-parameter tool with no output schema, the description covers the essential purpose. However, it does not describe the structure of the returned patterns, any limitations (e.g., language support), or how to interpret results, leaving an agent with only partial context for effective invocation and 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?

Schema coverage is 100% (the single 'text' parameter has a description). The description adds that it analyzes text, which aligns with the schema but provides no additional nuance about format, encoding, or expected content beyond what the schema already states.

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?

States a clear verb (analyze) and resource (text for AI slop indicators), and explicitly lists three analysis categories. It distinguishes functionally from siblings (this analyzes, others provide rules/examples), though it doesn't name them directly, so it falls just short of a 5.

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?

No explicit when-to-use or alternative routing is provided. The purpose implies this tool is for analyzing text, while get_human_writing_rules and get_slop_examples would likely be used for reference materials, but the description does not state this or offer any conditions for selection.

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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