to_train_case
Convert text to Train-Case.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to convert |
TinyFn
by io.tinyfn
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose any behavioral traits (e.g., handling of punctuation, capitalization rules, edge cases). The agent receives no insight beyond the basic conversion claim.
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?
Description is a single sentence with no waste. It is appropriately concise for a simple tool, though it could benefit from a brief example.
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 case conversion, the description provides the core purpose but lacks examples or definition of 'Train-Case'. Given the tool's simplicity, it is minimally acceptable but not fully informative.
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% with one parameter 'text'. The description adds the target case format (Train-Case), which is a slight improvement over the schema's generic 'Text to convert'. Baseline 3 is appropriate as the description adds marginal 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?
Description clearly states verb (Convert) and resource (text to Train-Case). It specifies the output format, making the purpose straightforward. However, it does not differentiate from sibling case conversion tools, which share similar phrasing.
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 vs alternatives like to_kebab_case, to_snake_case, etc. The description lacks context about desired output format or special cases.
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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Glama MCP Gateway
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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.