generate_uuidsGenerate cryptographically random UUID v4 values.
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | How many UUIDs to generate, 1 to 100 (default 1) |
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | ||
| result | No | The result, when it is not an object | |
| values | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / count / descriptionAdded value: +"How many UUIDs to generate, 1 to 100 (default 1)"Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "https://json-schema.org/draft/2020-12/schema",
+ "additionalProperties": {},
+ "properties": {
+ "format": {
+ "type": "string"
+ },
+ "result": {
+ "description": "The result, when it is not an object"
+ },
+ "values": {
+ "items": {},
+ "type": "array"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds meaningful context by specifying that the randomness is cryptographic and that values conform to the v4 variant. No contradiction with annotations.
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?
A single, front-loaded sentence communicates the core function with zero filler. Every word earns its place and no additional structure is needed for such a simple tool.
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 stateless utility with one well-documented parameter, an output schema, and annotations covering the safety profile, the description is completely sufficient. Return values are covered by the output schema, so no further explanation is required.
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 coverage is 100% for the single parameter (count), including type, default, min/max, and a description. The tool description adds no parameter-specific meaning beyond what the schema already provides, so the baseline 3 applies.
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 (Generate) and a concrete resource (cryptographically random UUID v4 values). It clearly distinguishes this utility from all the listed sibling tools, none of which produce random identifiers.
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 implies its usage: call it whenever random UUIDs are needed. However, it does not explicitly state when to use it versus alternatives or mention any exclusions, though there are no closely related sibling tools to disambiguate.
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.