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Glama

TinyFn

by io.tinyfn

text_similarity

Calculate similarity between two texts (Jaccard similarity).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
text1YesFirst text
text2YesSecond text

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
text1_wordsYesUnique word count in text1
text2_wordsYesUnique word count in text2
common_wordsYesNumber of words shared between both texts
jaccard_similarityYesJaccard similarity coefficient (0-1)
similarity_percentYesSimilarity as a percentage (0-100)

Schema Changelog

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

  1. First observed

TDQS

B3/5.0
Behavior2/5

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

The description lacks details on tokenization (e.g., case sensitivity, whitespace handling, whether characters or words are compared). With no annotations, the agent needs this information to use the tool correctly.

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

Conciseness3/5

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

The single sentence is concise but underspecified. It front-loads the purpose but omits important usage details, making it too brief for a production tool.

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

Completeness2/5

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

Despite having an output schema, the description does not explain the return type or range. Edge cases (empty strings, identical texts) are unaddressed. With no annotations, this is insufficient.

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 coverage is 100%, so baseline is 3. The description adds the Jaccard algorithm context but does not clarify parameter details (e.g., whether inputs are normalized). Thus, it adds marginal value.

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 specifies the verb 'calculate', the resource 'similarity between two texts', and the method 'Jaccard similarity'. This clearly distinguishes it from other text comparison tools like levenshtein_distance.

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. Given the large number of sibling tools, the description should include context like 'Use for set-based similarity; for edit distance, use levenshtein_distance instead.'

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