Parse a citation («Art. 7 Abs. 1 lit. b LSV») into act, article eId and path proposal: use to turn a quoted Fundstelle into what read_article can open. hint.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | A citation in plain text, e.g. «Art. 7 Abs. 1 lit. b LSV», «Anhang 3 Ziff. 2 LSV», «Art. 8 EMRK i.V.m. Art. 36 BV», «SR 832.10». |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, and the description does not contradict that. It adds useful behavioral context by describing the parsed output components and the tool's relationship to read_article, which goes beyond the annotation alone. It does not cover error handling or invalid input behavior, but the read-only safety profile lowers the burden.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loads the core purpose, with a helpful usage pointer to read_article. The trailing 'hint.' adds no clear value and slightly weakens the otherwise clean structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter, read-only parser, the description sufficiently explains what input is expected and what kind of output is produced. With no output schema, naming the output components is important and done here. Minor gaps remain around exact output shape and invalid-citation behavior, but they are not blocking for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides 100% coverage of the single 'text' parameter with concrete examples and a clear description. The tool description rephrases this as 'a citation' and 'quoted Fundstelle' but does not add meaningful semantic detail beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific action ('Parse'), a specific resource ('a citation'), and a concrete output ('act, article eId and path proposal'). It also distinguishes itself from the sibling read_article by describing how the parsed output feeds into that tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says to use it 'to turn a quoted Fundstelle into what read_article can open', giving a clear when-to-use scenario. It also names read_article as the downstream consumer, but it does not mention alternative tools or explicitly state when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tools are strongly namespaced and most have a clear 'use for' hint, so an agent can usually select correctly. A few near-neighbour pairs, such as fedlex.get_citations vs fedlex.get_references and facts.badge vs facts.latest, require careful reading but are still distinguishable.
The <domain>.<snake_case_action> style is consistent and the fedlex/lindas families are predictable. However, several noun-style names such as meta.tools, lindas.observations, and facts.badge deviate from the verb_noun pattern, and the variety of get_/read_/list_/find_ verbs adds minor noise.
With 52 tools, this is a very large MCP surface for one server; while each tool appears individually purposeful, the combined set is heavy for an agent to explore and select from. The gateway would be more manageable split into separate legal, data, and meta servers.
The Fedlex cluster covers the full legal-research workflow: search, version resolution, reading, comparison, citation checking, history tracing, consultations, and official publications. The LINDAS cluster covers cube discovery, schema inspection, filtering, and label resolution, and the directory/meta tools complete the capability-discovery loop. There are no obvious dead ends or critical missing operations.