Explore a JOLux node's edges (predicates and neighbours, both directions, capped): use to debug what the graph holds about an IRI; never as proof. hint.
| Name | Required | Description | Default |
|---|---|---|---|
| iri | Yes | Any Fedlex IRI (https://fedlex.data.admin.ch/…). | |
| limit | No | Edges per direction (default 20, at most 50). |
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 already declare readOnlyHint=true. The description adds behavioral context beyond that: edges are returned in both directions and are capped, and the result is diagnostic rather than authoritative. This helps the agent understand the tool's intent and limitations.
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?
A single sentence conveys the operation, scope, cap behavior, intended use, and an important caveat. There is no filler or repetition of schema content.
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?
The tool is simple, read-only, and its purpose is well described. There is no output schema, but the description already explains that the result contains predicates and neighbours in both directions, capped. Minor details about the exact response shape are not essential for selecting and invoking this tool.
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?
Schema description coverage is 100%, so the schema fully documents both 'iri' and 'limit'. The description adds graph-oriented context but does not need to repeat parameter details. Baseline 3 is appropriate.
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?
States a specific verb ('explore'), a precise resource ('a JOLux node's edges'), and the exact scope: predicates and neighbours, both directions, capped. This clearly distinguishes it from sibling getter tools that retrieve specific documents or metadata.
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?
Explicitly says when to use it: 'use to debug what the graph holds about an IRI'. It also gives a clear exclusion, 'never as proof', which prevents misuse. It does not name a specific alternative tool, but the use case is clear enough.
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.