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io.github.dfch/biz-dfch-asdste100mcp

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by dfch

rules_overview

Read-only

Get a low-token, per-rule overview of the ASD-STE100 ruleset to identify rule IDs, types, sections, and categories before drilling into details.

Instructions

Return a lightweight, per-rule overview of the ruleset.

Use this for a cheap, low-token summary of what rules exist before drilling into rules_find or rules_examples for a specific rule; each result carries only the rule's id, type, section, category, name, and (optionally) summary, plus counts/flags about its content items rather than the content items themselves.

Parameters

section: When given, only consider rules in this exact section (case-insensitive). category: When given, only consider rules in this exact category (case-insensitive). type_: When given, only consider rules of this exact type (e.g. "rule", to exclude recommendations and informational blocks). brief: When True (default), omit the summary to keep the payload small. When False, include the full summary.

Returns

list[RuleOverview] One overview per matching rule, in the ruleset's current order (natural id order by default).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
briefNoWhen true (default), omit the summary to keep the payload small.
type_NoOnly consider entries of this exact type, e.g. 'rule' (excludes recommendations and information blocks).
sectionNoOnly consider rules in this exact section (case-insensitive).
categoryNoOnly consider rules in this exact category (case-insensitive).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

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 the description adds meaningful behavioral detail: the tool returns only lightweight summaries with counts/flags rather than content items, omits summaries when brief=true, and returns results in ruleset order. This goes well beyond the safety hints.

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 with a front-loaded purpose, a usage pointer, a Parameters section, and a Returns section. Every sentence carries useful information, and nothing is redundant padding.

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 overview tool with four optional parameters and an output schema, the description fully covers purpose, usage context, parameters, return shape, and ordering. An agent has everything needed to call it correctly.

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

Parameters3/5

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

Schema coverage is 100%, so the baseline is 3. The description repeats the schema's parameter explanations (case-insensitive, exact section/category, type_ example, brief default) without adding meaning beyond what the input schema already provides.

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 and resource: 'Return a lightweight, per-rule overview of the ruleset.' It also clarifies what the result does NOT contain ('rather than the content items themselves'), which distinguishes it from siblings like rules_find and rules_examples.

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?

It explicitly says to use this tool 'for a cheap, low-token summary of what rules exist before drilling into rules_find or rules_examples'. This names concrete alternatives and gives a clear condition for choosing this tool over them.

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