Convert a slug back to readable text.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Slug to convert back to text | |
| separator | No | Word separator in slug | - |
| capitalize | No | Capitalize words |
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| text | 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?
With no annotations, the description must fully detail behavior. It only states 'Convert a slug back to readable text' without explaining how the conversion works (e.g., replacing separators with spaces, handling capitalization). No mention of edge cases, permissions, or return format. This is insufficient for a tool with 3 parameters.
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 extremely concise at 8 words, front-loading the purpose. Every word is informative. However, it could be slightly expanded with key behavioral details without losing conciseness.
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 the tool has 3 parameters, an output schema, and no annotations, the description is minimally complete. It covers the core purpose but lacks details on return values, behavior overrides, and edge cases. The output schema may cover return format, but description adds no context.
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?
The input schema has 100% coverage for parameter descriptions. The tool description adds no extra meaning beyond the schema; it does not explain parameter interactions or provide examples. Baseline 3 is appropriate.
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 clearly states the tool's sole purpose: converting a slug back to readable text. This is specific and directly distinguishes it from the sibling tool 'slugify', which does the inverse. The verb 'convert' and the resource 'slug' are well-chosen.
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?
The description does not explicitly state when to use this tool versus alternatives like 'slugify', 'is_valid_slug', or case conversion tools. Usage is implied by the name and inverse relationship, but no when-not or alternative guidance is provided.
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.
