Convert text to URL-friendly slug.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to convert to slug | |
| lowercase | No | Convert to lowercase | |
| separator | No | Word separator | - |
| max_length | No | Maximum slug length |
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| length | Yes | ||
| original | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. The description only states the basic conversion without mentioning edge cases, character handling, or the effects of its parameters (like lowercase, separator, max_length). This leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, concise and front-loaded. It efficiently conveys the core purpose without wasted words. However, it is perhaps too brief for a tool with four parameters, but given the simplicity, it earns a 4.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool has 4 parameters and an output schema exists, the description is minimally complete. It conveys the essential purpose but lacks contextual details such as typical use cases, return format hints, or integration notes. For a simple conversion tool, it is adequate but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the existing parameter descriptions in the input schema already document each parameter. The tool description adds no additional semantic context or examples beyond what the schema provides, meeting the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Convert text to URL-friendly slug' clearly states the verb and resource, distinguishing it from many sibling tools that convert to different cases or formats. However, the sibling list includes both 'slug' and 'slugify', which may cause confusion since they likely serve the same purpose, but the description itself does not differentiate them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 vs alternatives. The sibling set includes many string conversion tools (e.g., kebab_case, snake_case) that could be used similarly, and without usage notes, an AI agent may struggle to select the correct tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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 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.
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.
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.
