mcp

analyze_readability

Analyze a website URL for content readability using Flesch Reading Ease.

Extracts plain text from HTML and computes readability metrics including
Flesch Reading Ease score, Flesch-Kincaid grade level, reading time,
and word/sentence statistics.

Grade Scale (web-optimized):
- A (60-100): Easy, 6th-8th grade — ideal for web content
- B (50-59): Fairly easy, some high school
- C (30-49): Standard, college level
- D (10-29): Difficult, graduate level
- F (0-9): Very difficult, professional/academic

Results are cached for fast subsequent lookups.
Rate limited to 1 request per minute per domain.

Args:
    url: The website URL to analyze (e.g., "https://example.com").

Returns:
    Readability analysis with:
    - url: The analyzed URL
    - score: Flesch Reading Ease score (0-100, higher = easier)
    - grade: Letter grade (A-F)
    - flesch_kincaid_grade_level: US school grade level equivalent
    - reading_time_minutes: Estimated reading time in minutes
    - word_count: Total word count
    - sentence_count: Total sentence count
    - difficult_words: Count of difficult/uncommon words
    - cached: Whether result was from cache

Input Schema

TableJSON Schema

Name	Required	Description	Default
`url`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.4/5.0

Behavior5/5

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

With no annotations provided, the description fully carries behavioral transparency. It explicitly discloses that the tool extracts plain text from HTML, computes multiple metrics, caches results, and is rate-limited to 1 request per minute per domain.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear opening statement followed by organized sections for grade scale, arguments, and return values. It is somewhat lengthy due to the detailed grade scale, but every part contributes necessary information without fluff.

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

Completeness5/5

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

The description covers the tool's purpose, extraction behavior, caching, rate limits, parameter semantics, and a complete list of return fields. For a single-parameter tool, this is fully complete even with the output schema present.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema only defines a required 'url' string with no description. The description compensates by explaining 'The website URL to analyze' and providing an example, adding meaningful semantic context beyond the raw schema.

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 states 'Analyze a website URL for content readability using Flesch Reading Ease', providing a specific verb, resource, and metric. This clearly differentiates it from sibling tools like analyze_seo or analyze_wcag, which target different analyses.

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 description explains what the tool does and its caches/rate limits but does not explicitly state when to use it versus alternatives or provide exclusions. Usage context is implied by the name and purpose but not directly compared to sibling tools.

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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TDQS

A4.6/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: analyze_aeo, analyze_eeat, analyze_garm, analyze_readability, analyze_seo, and analyze_wcag each target a specific evaluation dimension, while classify_url, find_similar_sites, and select_advertiser serve different auxiliary functions. The audit tool is explicitly positioned as a combined report, so there is no ambiguity about overlaps.

Naming Consistency4/5

Most tools follow a clear verb_noun pattern (analyze_aeo, classify_url, find_similar_sites, select_advertiser). The single exception is 'audit', which is a bare verb and breaks the established convention, making the set slightly inconsistent.

Tool Count5/5

With exactly 10 tools, the set is well-scoped for a website analysis and advertising platform. Each tool covers a distinct aspect of the domain without redundancy, and the count is comfortably within the ideal 3-15 range.

Completeness5/5

The tool surface covers the full lifecycle of website evaluation: classification, multiple analysis dimensions (SEO, EEAT, AEO, readability, WCAG, GARM), competitive analysis, and advertiser matching. The audit tool ties everything together, leaving no obvious gaps for the stated purpose.

Resources

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