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EUR-Lex

by io.github.pipeworx-io

Get Article

get_article
Read-onlyIdempotent

Full text of one or more Articles of an act, with a verifiable EUR-Lex citation URL for each. THE article-level grounding tool: quote GDPR Art. 17, AI Act Art. 6, etc. Accepts a single number ("17"), a comma list ("5,6,17") or a range ("5-9").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoISO 639-2/B language code (default "eng").
celexYesCELEX id or a known alias (e.g. "GDPR").
articleYesArticle selector: "17", "5,6,17", or "5-9". Suffixed numbers like "9a" are supported.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds behavioral context: it returns 'full text' and 'verifiable EUR-Lex citation URL', and explains input format flexibility. No contradictions.

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?

Two concise sentences front-load the purpose, then provide usage examples and selector formats. Every sentence adds value with no redundancy.

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?

Despite no output schema, the description adequately covers return items (full text and citation URL) and input parameters. Given low tool complexity and full schema coverage, it provides sufficient context for an agent to use the tool correctly.

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?

Schema coverage is 100%, so the description doesn't need to explain all parameters, but it adds value by detailing how the 'article' parameter accepts single numbers, comma-separated lists, and ranges with suffix support, which goes beyond the schema's brief description.

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 it returns full text with citation URLs, uses specific verb 'get' and resource 'article', and distinguishes from siblings like list_articles or get_document by highlighting it as 'THE article-level grounding tool' for quoting specific articles.

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?

The description provides explicit guidance on article selector formats (single number, comma list, range) and gives examples (GDPR Art. 17, AI Act Art. 6). It implies primary use for article-level grounding but lacks explicit when-not-to-use or alternative tool recommendations.

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

All EUR-Lex toolsInspect
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TDQS

A3.7/5.0
Disambiguation1/5

Multiple tools appear to do nearly the same thing: ask_pipeworx, ask_ipeworx_beta (explicitly identical at the moment), ask_pipeworx_grounded, deep_research, and validate_claim all route questionanswering in a very similar way. Even with long descriptions, the sheer number of overlapping query/research/analysis tools (ai_visibility_check vs scan_comperitor_ai_presence, all polymarket_*) would make an agent uncertain which to call.

Naming Consistency2/5

The set uses snake_case everywhere but that is the only consistent part. There is a mess of verb_noun patterns, noun_verb patterns (cjeu_search vs search_legislation, cj_judgment vs get_document), bare noun phrases (entity_profile, compliance_index, pipeworx_feedback, polymarket_edges), and verb phrases (ask_ipeworx, generate_elms_txt, resolve_entry). A user cannot predict whether the noun comes first, so naming is readable but not predictable.

Tool Count2/5

39 tools is far over the 25+ threshold for a coherent set, and a large number of them (predictor markets ten, AI visibility, memory, subscriptions, pipework meta-tools) are outside the EUR-Lex legal research domain. The total count suggests a bundled everything-server rather than a focused legal-research MCP. It is not extreme enough for a 1 because 39 is still within a region where a broader meta-pipework suite could plausibly exist — but it's still too many.

Completeness4/5

For the EUR-Lex domain, the legal tools are nearly complete: search_legislation + compliance_index locate acts, get_metadata/list_articles/get_article/get_document read them, and cjeu_search/cjeu_judgment cover case law. Missing links that would make it fully seamless are amendment tracking, cross-references and direct CELEX/EURL-Lex citation search integration, but all basic 'find and read an act or judgment' workflows are supported.

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