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API Hub MCP — 337+ Tools

by scotia1973-bot
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

analyze_readability

Calculate Flesch-Kincaid readability scores and grade levels to assess text difficulty and reading ease.

Instructions

Calculate Flesch-Kincaid readability score, grade level, and reading ease.

Parameters:
    text — Text to analyze for readability.

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": "analyze_readability"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.4.1

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries full burden. It explains the calculations but lacks details on edge cases, limitations (e.g., text length, language), or output structure. Since it is a simple calculation, transparency is adequate but minimal.

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 extremely concise: a single sentence for the action followed by a one-line parameter definition. No extraneous information, perfectly front-loaded.

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?

The tool is simple with one parameter and an output schema present. The description covers the core functionality. Minor missing context (e.g., language support, no side effects) but overall sufficient for a straightforward calculation tool.

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 0%. The description adds 'Text to analyze for readability' for the 'text' parameter, which is a basic semantic improvement over the schema title alone. However, it provides no additional constraints or formatting details.

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 explicitly states 'Calculate Flesch-Kincaid readability score, grade level, and reading ease', providing a specific verb and resource. It clearly distinguishes itself from sibling readability tools like 'fry_readability' or 'gunning_fog_score' by naming the exact formula.

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?

No guidance on when to use this tool versus alternatives. Given the large number of sibling readability tools, explicit context for selection is missing.

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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