uuid_generatorGenerate one or more random version 4 (RFC 4122) UUIDs. Runs on smart-tools.xyz.
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | How many UUIDs to generate, 1–1000 (default 1) | |
| locale | No | Language for the source_url link (default en) |
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 carries the full burden. It discloses that UUIDs are random version 4 and that multiple can be generated. However, it does not explain output format, side effects, or any other behavioral traits beyond what the schema already implies. The additional 'Runs on smart-tools.xyz' is not behavioral.
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 first sentence is front-loaded and highly informative. The second sentence 'Runs on smart-tools.xyz' is not needed for tool selection or invocation, adding minor noise. Overall, the description is short and mostly efficient.
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 tool with full schema coverage and no output schema, the description is adequate. It states the core function and randomness. It does not describe the output structure, but the agent can reasonably infer UUID generation outputs. The missing output schema is a gap, but the tool is low-complexity.
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% for both parameters. The description does not repeat parameter details but also does not add extra meaning beyond the schema. Baseline of 3 is appropriate since the schema handles the semantics.
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 uses a specific verb 'Generate' with a clear resource 'random version 4 (RFC 4122) UUIDs' and scopes the count ('one or more'). This distinguishes the tool from siblings like password_generator or random_number_generator, which serve different purposes.
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?
Usage context is implied by the purpose: an AI agent would understand to use this tool when a UUID is needed. However, there is no explicit when-to-use, when-not-to-use, or mention of alternatives (e.g., random_number_generator).
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.
Most tools are clearly distinct, but there are notable overlaps. 'hash_generator' and 'file_hash' effectively do the same thing (SHA hashing of text), and several CSV/TSV converters (csv_to_json, csv_to_sql, table_to_csv, markdown_table) have similar input handling, though outputs differ. The sheer number of converters is clear, but these near-duplicates create some ambiguity.
Names follow a generally consistent pattern: lowercase with underscores, often ending in 'converter', 'calculator', or 'generator'. A few names like 'base64', 'lorem_ipsum', and 'transliteration' deviate from the suffix pattern, but the naming style is uniform and predictable overall.
With 73 tools, this server is far over the typical well-scoped range. While the broad utility-toolkit purpose might justify a larger set, 73 is unwieldy and pushes well into 'too many' territory, making it harder for an agent to select the right tool efficiently.
The server covers a remarkably wide range of common utilities: unit conversions, calculators, text transformations, development helpers (JSON, regex, JWT, subnet), and generators. Minor gaps exist (e.g., currency converter, time zone converter), but for a general-purpose toolkit, it is largely comprehensive.