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io.github.dfch/biz-dfch-asdste100mcp

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by dfch

word_match

Read-only

Search the ASD-STE100 vocabulary with a regular expression to find terms sharing a prefix, suffix, or other pattern. Paginated results include the total match count and truncation flag.

Instructions

Search the vocabulary using a regular expression pattern.

Return all entries whose term matches. Use it to find all words with a common prefix or pattern (e.g. ^de or .*tion$). A broad pattern can match a large part of the vocabulary, so results are paginated.

Parameters

term: A regular-expression pattern (e.g. "util.*"). max_results: The maximum number of matching vocabulary entries to return (default 25). offset: The number of matching vocabulary entries to skip before returning results, for pagination (default 0).

Returns

WordResult results holds the (possibly empty) page of matching vocabulary entries after applying offset and max_results. total is the full match count before pagination, and truncated tells the caller whether more matches exist beyond this page.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
termYesThe term to look up.
offsetNoThe number of matching entries to skip before returning results, for pagination.
max_resultsNoThe maximum number of matching entries to return.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalYes
offsetYes
resultsNo
truncatedYes
max_resultsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.0.0

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds meaningful behavioral context beyond that: results are paginated, broad patterns can match large portions of vocabulary, and the response includes total count and a truncated flag. It also clarifies how offset and max_results affect the returned page.

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?

The description is well-structured and front-loaded: a one-sentence purpose, a brief usage note with examples, then compact parameter and return sections. Every sentence earns its place, with no redundant filler.

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?

For a read-only regex search tool, the description covers the operation, pagination behavior, parameter semantics, and return structure (results, total, truncated). This is sufficient for an agent to invoke the tool correctly without needing further clarification.

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?

The input schema has 100% coverage and fairly clear descriptions, but the tool description adds critical semantics: 'term' is explicitly a regular-expression pattern with an example ('util.*'), rather than just 'The term to look up.' The pagination semantics of offset and max_results are also reinforced in prose.

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 opens with a specific verb and resource: 'Search the vocabulary using a regular expression pattern' and 'Return all entries whose term matches.' This clearly distinguishes word_match from likely siblings like word_fuzzy or word_find by making the regex mechanism explicit.

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

Usage Guidelines4/5

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

It explicitly states when to use the tool: 'Use it to find all words with a common prefix or pattern' and gives concrete regex examples. It does not name alternatives or exclusions, so it stops short of full when-not guidance, but the intended use case is unambiguous.

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