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get_norm_text

Extracts clean, structured Markdown text of a Russian legal article, removing ads, banners, and navigation to provide reliable source text for analysis.

Instructions

Извлечение чистого структурированного текста статьи нормативно-правового акта РФ в формате Markdown (без рекламного мусора, баннеров и навигации).

Input Schema

NameRequiredDescriptionDefault
actNoНаименование акта (например: "Трудовой кодекс РФ" или "ЗоЗПП").
urlNoПрямая ссылка на документ/статью (из результатов search_legislation).
articleNoНомер статьи (например: "ст. 80" или "18").
raw_htmlNoСырой HTML текст (для тестирования или оффлайн-парсинга).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.4/5.0
Behavior3/5

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

No annotations exist, so the description carries the full burden: it discloses the useful behavioral trait that output is clean Markdown with ads, banners, and navigation stripped out. However, it doesn't say whether the tool fetches live over the network or how it behaves when the fetch fails and raw_html is supplied, so important operational context is missing.

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?

It is a single tight sentence with zero waste, and the key deliverable (clean structured Markdown text) is front-loaded before the parenthetical about what gets removed.

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?

It has four optional parameters and no output schema, so the description needs to explain the return shape on its own; it does so by specifying Markdown and what is cleaned out. It is still a bit thin on which parameters trigger live fetching versus offline parsing, but the essential information for calling it is present.

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%, so all four parameters are already documented in the schema, giving a baseline of 3. The description adds only the output-format detail and does not clarify how act, url, article, and raw_html relate to each other or which combinations are valid.

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

Purpose4/5

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

The description names a specific action (extraction) applied to a specific resource (the article text of a Russian normative legal act) and even specifies the output format (Markdown). It doesn't explicitly differentiate itself from search_legislation, which also touches legislation, but the extraction/cleanup focus is distinct enough that an agent can identify it.

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

Usage Guidelines2/5

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

The description never states when to use this tool rather than search_legislation or check_norm_status. The only usage hint ('url from search_legislation results') appears in the schema, not the description, so the description itself provides no routing guidance or preconditions.

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