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Glama

TinyFn

by io.tinyfn

readability_score

Calculate readability scores (Flesch-Kincaid, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText to analyze

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
difficultyYesHuman-readable difficulty label: Very Easy, Easy, Moderate, Difficult, or Very Difficult
word_countYesTotal number of words
text_lengthYesTotal character count of the input text
sentence_countYesTotal number of sentences
syllable_countYesTotal number of syllables
flesch_reading_easeYesFlesch Reading Ease score (0-100, higher = easier)
flesch_kincaid_gradeYesFlesch-Kincaid grade level (US school grade)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It only mentions calculating scores but does not specify idempotency, handling of long texts, or what exactly is returned (e.g., multiple scores). Minimal transparency.

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 a single sentence that efficiently conveys the main action and an example. It is front-loaded and wastes no words, though it could benefit from a bit more detail.

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 tool with one parameter and an output schema, the description is somewhat adequate. However, it does not list the specific readability scores returned, relying on the output schema. More completeness would be helpful.

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?

The schema already describes the 'text' parameter as 'Text to analyze'. The description adds that it is analyzed for readability, which provides additional context. Since schema coverage is 100%, baseline is 3, and the description adds some but not extensive value.

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?

The description clearly states the tool calculates readability scores, specifically mentioning Flesch-Kincaid as an example. It distinguishes itself from sibling text analysis tools by focusing on readability, a distinct metric.

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 is provided on when to use this tool versus alternatives like basic_sentiment or extract_keywords. The description lacks any context for appropriate usage scenarios.

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

C2.3/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as multiple random generators (random_integer, random_number), duplicate hashing functions (hash_md5, md5_checksum), and near-identical tools (compare, compare_2, compare_decimals). The sheer number of tools and lack of clear boundaries make it difficult for an agent to differentiate.

Naming Consistency1/5

Naming is highly inconsistent. There are duplicate tools with different names (camel_case vs to_camel_case, slug vs slugify), arbitrary suffixes like '_2', and mixing of patterns (e.g., generate_password vs password_entropy). No clear convention is followed.

Tool Count1/5

With 572 tools, the server is massively overpopulated for any coherent purpose. It includes trivial endpoints (true_endpoint, null, hello_world) and numerous duplicates, far exceeding a well-scoped utility set.

Completeness2/5

While the server covers many domains (math, strings, dates, colors, etc.), the presence of duplicate and trivial tools indicates a lack of thoughtful curation. There are gaps in basic operations (e.g., no dedicated file or network tools), and many tools are redundant.

Resources