generate_slugConvert text into a URL-safe slug, handling unicode and diacritics deterministically.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The title or phrase to turn into a URL slug |
| Name | Required | Description | Default |
|---|---|---|---|
| slug | No | ||
| result | No | The result, when it is not an object | |
| slugLength | No | ||
| originalLength | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / text / descriptionAdded value: +"The title or phrase to turn into a URL slug"Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "https://json-schema.org/draft/2020-12/schema",
+ "additionalProperties": {},
+ "properties": {
+ "originalLength": {
+ "type": "number"
+ },
+ "result": {
+ "description": "The result, when it is not an object"
+ },
+ "slug": {
+ "type": "string"
+ },
+ "slugLength": {
+ "type": "number"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond annotations: it explicitly promises deterministic handling of unicode and diacritics, which informs an agent about consistency and input normalization behavior.
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?
One sentence with no filler: it states the core transformation, the target format, and two important behavioral qualifiers (unicode handling and determinism). Every element earns its place and the main purpose is front-loaded.
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?
For a simple, read-only, idempotent transformation tool with a single well-documented parameter and an output schema available, the description is largely complete. It could be slightly more specific about slug formatting conventions (e.g., separator or lowercase behavior), but the combination of description, schema, and annotations is sufficient for correct selection and invocation.
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 single parameter 'text' is fully described in the schema (100% coverage) as 'The title or phrase to turn into a URL slug'. The description does not add parameter-specific detail beyond the schema, but it doesn't need to because the schema already carries the meaning. 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 states a specific verb and resource: 'Convert text into a URL-safe slug'. It also adds meaningful scope by mentioning unicode and diacritics handling, which sets it apart from a generic url_encode sibling. An agent can immediately understand what the tool produces.
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 clearly implies the use case — turning text into a URL-safe slug — but it does not explicitly state when to choose this over alternatives like url_encode or other text-conversion tools. There is no when-not-to-use guidance, though the purpose is self-evident from the phrasing.
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.
Every tool targets a distinct resource or action, and the detailed descriptions clearly separate near neighbors like generate_test_bsn versus generate_brp_test_data, read_page versus url_screenshot versus url_to_pdf, and image_compress/convert/resize. Even with 40 tools, there is no real boundary-blurring overlap.
All names are snake_case and readable, but the set mixes conventions: verb_noun (generate_*, validate_*), noun_verb (pdf_merge, image_resize), conversion-style names (csv_to_json, html_to_pdf), and bare nouns (base64, qr_code_png). The groups are recognizable, but there is no single predictable pattern.
Forty tools is an oversized surface for an agent to consider on every call, well above the point where tool selection cost starts to hurt. The broad purpose explains the count, but many one-off utilities could be grouped or exposed selectively.
The server covers many domains—encoding, Dutch test data, image/PDF handling, memory, and workflows—but several categories are partial: there are no reverse conversions like json_to_csv or html_to_markdown, no PDF text extraction, and no workflow create/update/delete tools. Agents can work around some gaps, but notable operations are missing.