SKILL.md Lint

Regex Test

regex_test

Read-onlyIdempotent

Test a regular expression against sample strings. FREE.

Typical input {"pattern": "v(\d+)", "samples": ["v12", "beta"]} returns {"pattern": ..., "results": [{"sample": "v12", "match": true, "matched_text": "v12", "groups": ["12"]}, {"sample": "beta", "match": false, ...}], "match_count": 1}.

Use to confirm a pattern matches what you expect before shipping it. Not for validating JSON (json_validate). The pattern is compiled and run in a short-lived, CPU-capped child process, so a pattern that backtracks catastrophically is stopped instead of stalling the server. Errors: on invalid, missing, or malformed input this tool never raises a protocol error; it returns {"error": ""} (for example {"error": "a sample is too long (max 10000 characters); shorten it and retry"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "pattern": "<pattern>",
      "samples": "<samples>"
    },
    "name": "regex_test"
  }
}

Input Schema

Name	Required	Description	Default
`pattern`	Yes	A Python-flavor regular expression as a string, e.g. "v(\\d+)".
`samples`	Yes	Strings to test the pattern against; only the first 50 are evaluated.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 14, 2026
- addedInput schema / properties / pattern / maxLength
```
Added value: +1000
```
- addedInput schema / properties / samples / maxItems
```
Added value: +50
```
First observedAug 17, 2026

TDQS

A4.8/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the annotations, it discloses execution isolation (short-lived, CPU-capped child process that stops catastrophic backtracking), the error contract (never raises a protocol error, returns {"error": ...} with a concrete example), and retry safety. The only redundancy is restating read-only/idempotent, which the annotations already cover.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose and the sibling exclusion are front-loaded, and the sandbox/error paragraph earns its place. However, the full input/output example is partially redundant given an output schema exists, and the read-only/idempotent sentence repeats the annotations.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Complete for a two-parameter read-only tool: purpose, alternative, sandboxing limits, input caps, and the exact error shape are all covered, and return values are handled by the output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so both parameters are already documented, but the worked example input clarifies how pattern and samples combine in practice. It adds modest value rather than new syntax or format detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ("Test a regular expression against sample strings") and explicitly names the sibling it is not for (json_validate), so an agent can distinguish it from siblings without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit use case ("confirm a pattern matches what you expect before shipping it") and an explicit exclusion with the alternative tool named ("Not for validating JSON (json_validate)"). Nothing is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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