eurlex-mcp-server

eurlex_structure

Read-onlyIdempotent

Retrieve the table of contents of an EU legal act with character offsets for each section. Use this map to fetch specific articles directly via eurlex_fetch.

Instructions

Returns the outline (table of contents) of an EU legal act — its chapters, sections, articles and annexes — each with a character offset into the document's plain text. Use it as a map for targeted reading: read an article's offset from the outline, then call eurlex_fetch(celex_id, format:"plain", offset, max_chars) with that offset to jump straight to that article instead of paging from the top of a long act. Identify the act by celex_id (e.g. "32024R1689"), eli, or oj_ref — provide exactly one. Each outline entry has: level (1=part/title/annex, 2=chapter, 3=section, 4=article), label (e.g. "Article 5", "CHAPTER III"), title (the heading's subtitle, e.g. "Prohibited AI practices"), and offset. total_headings is the full count; the returned list is capped at 300 for very large acts (truncated=true). Heading offsets are specific to the chosen language and to plain (tag-stripped) text — pass the SAME language to the follow-up eurlex_fetch call and keep format:"plain". Heading recognition covers English, German and French documents. For case-law documents (CELEX sector 6, e.g. CJEU judgments) the outline additionally lists each numbered judgment paragraph as "Paragraph N" (level 4); numbered-paragraph detection works in any language (it keys on the paragraph number, not heading words) — so you can get the offset of, say, paragraph 72 of a judgment and jump eurlex_fetch straight to it.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`eli`	No	European Legislation Identifier (ELI), short or full form, e.g. "reg/2016/679" or "http://data.europa.eu/eli/reg/2016/679/oj" (GDPR). Resolved to a CELEX ID via Cellar. Provide exactly one of celex_id, eli, or oj_ref.
`oj_ref`	No	Official Journal reference in the post-2023 scheme, e.g. "OJ:L_202401689" (AI Act). Resolved to a CELEX ID via Cellar. Provide exactly one of celex_id, eli, or oj_ref.
`celex_id`	No	CELEX identifier, e.g. "32024R1689" (AI Act). Provide exactly one of celex_id, eli, or oj_ref.
`language`	No	Language of the document to outline, as a Cellar 3-letter code (any of the 24 official EU languages, e.g. DEU, ENG, FRA). Heading labels are language-specific; the returned offsets index the plain text of THIS language, so pass the same language to the follow-up eurlex_fetch call.	ENG

Output Schema

TableJSON Schema

Name	Required	Description
`note`	No	Present only when no headings were found or the outline was truncated
`outline`	Yes	Headings in document order
`celex_id`	Yes	The resolved CELEX ID (echoed for the follow-up eurlex_fetch)
`language`	Yes
`returned`	Yes	Number of headings in `outline` (<= total_headings)
`truncated`	Yes	True when `outline` was capped below total_headings
`source_url`	Yes
`total_chars`	Yes	Length of the plain text the offsets index into (matches eurlex_fetch total_chars)
`total_headings`	Yes	Total headings detected before the returned-list cap

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv2.4.2Sep 7, 2026

removedInput schema / $schema

Removed value: -"http://json-schema.org/draft-07/schema#"

removedOutput schema / $schema

Removed value: -"http://json-schema.org/draft-07/schema#"

Changed1 schema field changedv2.3.0Jul 22, 2026
- changedInput schema / properties / language / default
```
Previous value: -"DEU"New value: +"ENG"
```
First observedv2.2.0Jul 10, 2026

TDQS

A4.8/5.0

Behavior5/5

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

The description discloses language-specific offsets, the dependency on plain text, the 300-entry cap with truncation flag, and the heading-recognition coverage, all beyond the read-only annotation. It also explains the paragraph-number detection logic for judgments, giving the agent accurate expectations.

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 richly informative but remains focused, covering purpose, usage, and key behavioral details without extraneous content. It is structured with clear separators, making it easy to scan. Slight repetition of parameter constraints is minor and acceptable.

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?

The description covers the tool's output structure, usage scenario, language handling, size limits, and case-law extensions, leaving no major gaps. It even specifies the follow-up call pattern, ensuring the agent knows how to apply the offsets.

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?

While the schema already describes all parameters, the description adds the crucial note that offsets are language-specific and advises passing the same language to the follow-up fetch call. It also clarifies the relationship between celex_id, eli, and oj_ref, requiring exactly one, which is echoed but reinforced.

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 clearly states the tool returns the outline (table of contents) of an EU legal act with character offsets, distinguishing it from other document tools. The verb 'returns' and the specific resource make the purpose unambiguous.

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 advises using this tool as a map for targeted reading, telling the agent to read an article's offset and follow up with eurlex_fetch. This gives clear when-to-use guidance, including the special case of case-law paragraphs.

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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