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io.github.dfch/biz-dfch-asdste100mcp

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by dfch

rules_toc

Read-only

Get a table-of-contents outline of the ASD-STE100 ruleset. Returns distinct section-category pairs in first-seen order, showing available sections and categories before diving into rule details.

Instructions

Return the distinct (section, category) pairs, in first-seen order.

Gives a table-of-contents style outline of the ruleset's structure, without any per-rule detail; useful to see which sections and categories exist before drilling into rules_overview, rules_by_section, or rules_by_category for a specific one.

Parameters

section: When given, only consider rules in this exact section (case-insensitive).

Returns

list[TocEntry] One entry per distinct (section, category) pair, in first-seen document order, where ids lists the ids of every rule/recommendation/information item in that (section, category), in document order.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sectionNoOnly consider rules in this exact section (case-insensitive).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.0.0

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already establish read-only safety. The description adds meaningful behavioral detail: distinct grouping, first-seen ordering, and exact section case-insensitive filtering, plus what each returned entry includes. This goes beyond what annotations convey.

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?

The description is well-structured with a clear opening sentence, then Parameters and Returns sections. It is reasonably concise, though the parameter explanation duplicates the schema and could be trimmed without loss.

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

Completeness4/5

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

For a simple optional-parameter tool with an output schema, the description covers purpose, usage context, and return semantics. It provides enough for an agent to call it correctly without additional explanation, and the output schema fills in structural details.

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 description coverage is 100%, and the description repeats the parameter's case-insensitive exact-section meaning without adding new semantic details. Baseline 3 is appropriate as the schema already fully documents the parameter.

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 explicitly states the tool returns distinct (section, category) pairs in first-seen order and characterizes it as a table-of-contents outline. This clearly distinguishes it from per-rule sibling tools by noting it gives no per-rule detail.

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 provides clear usage context by recommending this tool before drilling into rules_overview, rules_by_section, or rules_by_category. However, it stops short of explicitly saying when not to use it, though the 'without any per-rule detail' phrase implies that limitation.

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