regex_testerTest a JavaScript regular expression against text and return every match with its captured groups. Runs on smart-tools.xyz.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to test against | |
| flags | No | Regex flags, any of gimsuy (default g) | |
| locale | No | Language for the source_url link (default en) | |
| pattern | Yes | The regular expression source (no delimiters) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the core behavior (including returning captured groups) and the JavaScript flavor, but it does not mention side effects (though none are expected), error handling, or any rate limits. For a simple read-only testing tool, this is adequate but not rich.
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 concise and front-loaded with the main purpose. The second sentence ('Runs on smart-tools.xyz') is unnecessary for an agent to select or invoke the tool, making it slightly less than perfectly efficient. Still, the overall length is appropriate and easily scannable.
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's simplicity, the description is mostly complete. It explains the purpose, the input (pattern and text), and the output (matches with groups). Since there is no output schema, the description's mention of the return value is essential and provided. Minor gaps like invalid regex behavior are not critical for this type of tool.
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 baseline is 3. The description adds a small amount of semantic context by mentioning 'captured groups', which relates to pattern behavior, but it doesn't elaborate on flags or locale beyond what the schema already describes. No additional parameter meaning is provided.
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 'test' with a clear resource ('a JavaScript regular expression against text') and defines the output ('return every match with its captured groups'). This clearly distinguishes it from sibling tools, which are all calculators or converters unrelated to regex.
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 provides clear context for when to use the tool: when you need to test a JavaScript regex against text and see matches with groups. It does not explicitly name alternatives or exclusions, but given there are no sibling regex tools, such guidance is unnecessary. The purpose is unambiguous.
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.