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content-qa-mcp

content-qa-mcp

by Alvi-808
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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Readability check

readability_check

Analyze article prose for readability issues using the Flesch-Kincaid grade, long-sentence detection, and passive voice. Identify problem areas and receive actionable fixes to improve clarity and flow.

Instructions

Flesch-Kincaid grade, long-sentence and passive-voice findings for article prose. Plain text in; findings with fixes out.

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": "readability_check"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesArticle prose, plain text

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. 'Plain text in; findings with fixes out' clearly signals a side-effect-free analysis operation returning suggestions, and enumerates the three specific checks performed. It doesn't disclose edge cases (e.g., handling of non-prose input) but for a stateless analysis tool the core behavioral profile is well covered.

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 compact sentences with zero waste. The analysis types are front-loaded in the first sentence, and the input/output contract is packed into a pithy 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.

Completeness4/5

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

For a single-parameter analysis tool with 100% schema coverage and no output schema, the description is nearly complete: it states input format, the three checks, and the output shape (findings with fixes). The only minor ambiguity is what 'fixes' precisely means — whether suggestions or corrected text — but the core calling contract is clear.

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 description coverage is 100% — the single 'text' parameter is already documented as 'Article prose, plain text'. The description's 'Plain text in' is mostly redundant with the schema. Baseline 3 applies here since the schema fully handles parameter documentation and the description does not add meaningful syntax or format detail beyond it.

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 names a specific resource ('article prose') and a clear operation with concrete analysis dimensions: Flesch-Kincaid grade, long-sentence and passive-voice findings. This distinctly separates it from its siblings (qa_full_report, ai_tell_scan, seo_onpage_check), which cover different QA, AI and SEO concerns respectively. An agent can immediately tell what this tool produces.

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?

The purpose is clear enough that an agent can infer when to use it (readability analysis of prose), but the description gives no explicit when-to-use/when-not-to-use guidance and names no alternatives. Siblings cover different domains so confusion is unlikely, but no exclusions or selection criteria are stated.

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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