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test_regex

Run a JavaScript regular expression against a test string using a real regex engine. Returns all matches with capture groups and indices.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
flagsNog
patternYes

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It does add value: 'using a real regex engine' signals genuine evaluation rather than a mock or heuristic check, and the return behavior (all matches, capture groups, indices) is stated. However, it omits error behavior for invalid patterns, how flags alter matching, and any explicit statement that the operation has no side effects.

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

Conciseness5/5

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

Two sentences, roughly 30 words, with zero filler. The action is front-loaded, and both sentences earn their place: the first states what it does, the second states what it returns. 'Using a real regex engine' is non-redundant because it distinguishes genuine evaluation from simulation.

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

Completeness3/5

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

For a moderately simple tool with no output schema and no annotations, the description covers purpose, main parameters, and return shape. Gaps remain: the flags parameter is unexplained, error handling for invalid regex patterns is unspecified, and the structure of returned matches/indices is only vaguely described. Adequate for invocation but not exhaustive.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It maps 'regular expression' to pattern and 'test string' to text, adding real meaning for two of three parameters. But the flags parameter is never mentioned, even though the description's claim of returning 'all matches' depends on the default 'g' flag — leaving an agent to guess at flag semantics.

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?

The description states a specific verb ('Run') and resource ('a JavaScript regular expression against a test string'), with a clear behavioral qualifier ('using a real regex engine'). It also specifies the return value: all matches with capture groups and indices. None of the 35 sibling tools perform regex testing, so it stands clearly apart.

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

Usage Guidelines3/5

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

Usage context is implied: an agent would select this tool when it needs to evaluate a regex against sample text. However, there is no explicit when-to-use/when-not-to-use guidance, no exclusions, and no mention of alternative tools. The absence of a regex sibling lowers the stakes, but the description still leaves the decision entirely 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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TDQS

A3.6/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but some pairs could be confused: html_to_pdf vs url_to_pdf both produce PDFs, read_page vs url_screenshot both fetch a URL, and generate_test_bsn vs generate_brp_test_data both generate Dutch test data. Detailed descriptions mitigate most ambiguity, so agents can usually select correctly.

Naming Consistency5/5

All tool names follow a consistent snake_case convention with predictable verb_noun patterns (e.g., generate_*, validate_*, memory_*, url_*). The naming is uniform and makes each tool's function easily inferable from its name.

Tool Count2/5

35 tools is a heavy surface for any MCP server, even a general-purpose toolbox. The large number of niche Dutch-specific tools (BRP, UPA, BSN, holidays) may overwhelm agents and increase selection complexity. The server could benefit from splitting into smaller, focused servers.

Completeness3/5

The server covers a wide range of utilities, but there are notable gaps: no hash generation, no json_to_csv counterpart, and the workflow sub-domain only offers list/run without create/delete. Core utilities are present, but some obvious counterparts and lifecycle operations are missing.

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