The Text Readability Metrics API is a powerful tool for developers and content creators who want to evaluate the readability of written content. This API computes a set of readability metrics for a given text, including the Automated Readability Index, Coleman-Liau Index, Flesch–Kincaid Grade Level, Flesch Reading Ease, Gunning-Fog Index, SMOG score, and SMOG Index.
The Automated Readability Index (ARI) is a metric that measures the readability of a text by taking into account the average number of words per sentence and the average number of characters per word. The resulting score indicates the grade level required to understand the text.
The Coleman-Liau Index is a metric that measures the readability of a text by taking into account the average number of characters per word and the average number of sentences per 100 words. The resulting score indicates the grade level required to understand the text.
The Flesch–Kincaid Grade Level is a metric that measures the readability of a text by taking into account the average number of words per sentence and the average number of syllables per word. The resulting score indicates the grade level required to understand the text.
The Flesch Reading Ease is a metric that measures the ease of reading a text by taking into account the average number of words per sentence and the average number of syllables per word. The resulting score indicates how easy or difficult it is to understand the text.
The Gunning-Fog Index is a metric that measures the readability of a text by taking into account the average number of words per sentence and the percentage of words that have three or more syllables. The resulting score indicates the grade level required to understand the text.
The SMOG score and index are metrics that measure the readability of a text by taking into account the number of polysyllabic words in the text. The resulting score indicates the grade level required to understand the text.
The Text Readability Metrics API is easy to use, with simple REST API calls that return JSON-formatted results. Developers can use this API to evaluate the readability of written content, such as articles, essays, or blog posts. This can help content creators target specific audiences, improve the overall readability of their content, and provide a better user experience for readers.
Overall, the Text Readability Metrics API is a valuable tool for developers and content creators who want to evaluate the readability of written content. With its support for a variety of readability metrics, this API can help developers create intelligent applications that can understand the complexity and readability of written content, making it easier for users to access and understand information.
Pass the text and receive its readability metrics.
Content Creation: Content creators, such as bloggers or copywriters, can use the Text Readability Metrics API to ensure that their content is easy to read and understand. By analyzing their writing, they can identify areas that may be too complex and adjust their writing to target a specific audience.
Educational Applications: Educational applications can use the Text Readability Metrics API to evaluate the difficulty of educational texts and adjust the content based on the reading level of the user. By providing content that is tailored to a user's reading level, these applications can make educational material more accessible and engaging.
News Aggregators: News aggregators can use the Text Readability Metrics API to identify articles that are easy to read and prioritize for their users. By presenting articles that are easier to read, these applications can increase user engagement and improve the user experience.
Accessibility: The Text Readability Metrics API can be used to evaluate the readability of websites and applications to ensure that they are accessible to a wide range of users. By making content easier to read, users with cognitive disabilities or reading difficulties can better access information.
Language Learning: Language learning applications can use the Text Readability Metrics API to evaluate the difficulty of texts in a foreign language. By providing content that is tailored to the user's language level, these applications can make language learning more accessible and engaging.
Besides the number of API calls, there is no other limitation
Retrieve the readability metrics of the given text.
Get Metrics - Endpoint Features
Object | Description |
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text |
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{
"SMOG": 22.076,
"CHARACTERS": 372.000,
"SYLLABLES": 130.000,
"COLEMAN_LIAU": 20.119,
"WORDS": 61.000,
"FLESCH_READING": -4.418,
"ARI": 22.543,
"COMPLEXWORDS": 22.000,
"FLESCH_KINCAID": 21.453,
"SENTENCES": 2.000,
"GUNNING_FOG": 26.400,
"SMOG_INDEX": 21.166
}
curl --location --request POST 'https://zylalabs.com/api/1820/text+readability+metrics+api/1481/get+metrics?text=The word logorrhoea is often used pejoratively to describe prose that is highly abstract and contains little concrete language. Since abstract writing is hard to visualize, it often seems as though it makes no sense and all the words are excessive. Writers in academic fields that concern themselves mostly with the abstract, such as philosophy and especially postmodernism, often fail to include extensive concrete examples of their ideas, and so a superficial examination of their work might lead one to believe that it is all nonsense.' --header 'Authorization: Bearer YOUR_API_KEY'
Header | Description |
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Authorization
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[Required] Should be Bearer access_key . See "Your API Access Key" above when you are subscribed. |
No long-term commitment. Upgrade, downgrade, or cancel anytime. Free Trial includes up to 50 requests.
The API returns various readability metrics for the provided text, including scores such as SMOG, Flesch Reading Ease, and Gunning-Fog Index. Each metric indicates the grade level or complexity of the text, helping users assess readability.
Key fields in the response include SMOG, Flesch Reading Ease, ARI, Coleman-Liau, Flesch-Kincaid, Gunning-Fog, and metrics like WORDS, SENTENCES, CHARACTERS, and SYLLABLES. Each field provides specific insights into the text's readability.
The response data is structured in JSON format, with each readability metric represented as a key-value pair. This allows for easy parsing and integration into applications, enabling developers to utilize the metrics effectively.
The primary parameter for the endpoint is the text input, which should be a string containing the content to be analyzed. Users can customize their requests by varying the text length and complexity to evaluate different types of content.
Users can analyze the returned metrics to adjust their writing for specific audiences. For instance, a high Flesch Reading Ease score indicates easier readability, while a low SMOG score suggests the text is suitable for a broader audience.
Typical use cases include content creation for blogs, educational material assessment, news article prioritization, and accessibility evaluations. The API helps ensure that content meets the desired readability standards for its target audience.
The API employs established readability formulas, ensuring consistent and reliable calculations. Regular updates and testing against known benchmarks help maintain the accuracy of the metrics provided.
Users can expect a range of scores, with higher values indicating more complex text. For example, a Flesch Reading Ease score below 30 suggests very difficult text, while scores above 60 indicate easier readability.
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