Stage two of two-stage discovery: the full input schemas of the named tools, byte-identical to what tools/list carries, because both are read from the same router. An unknown id is refused by name rather than silently dropped.
| Name | Required | Description | Default |
|---|---|---|---|
| names | Yes | The capability ids whose input schemas are wanted, as stage one listed them. |
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?
With readOnlyHint already provided, the description adds useful behavior beyond annotations: schemas are byte-identical to tools/list, and unknown ids are refused by name rather than silently dropped. These are concrete, agent-relevant behavioral guarantees.
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?
Two sentences, with the core purpose front-loaded and no filler. The consistency guarantee and error behavior each earn their place in the second sentence.
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 read-only discovery tool with one well-documented parameter, the description covers the workflow position, return content, consistency property, and error handling. Nothing essential is missing 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 input schema already documents the single `names` parameter with 100% coverage, including that these are capability ids from stage one. The description reinforces 'named tools' but does not add significant new parameter-level meaning.
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 the exact action and resource: returning the full input schemas for named tools. Positioning it as 'stage two of two-stage discovery' immediately distinguishes it from stage-one listing tools, so an agent can tell what it is for.
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?
It clearly says this is the second stage of discovery, implying it should be used after a stage-one listing. It does not explicitly name an alternative tool to use instead, but the stage framing and schema requirement make the intended context clear.
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.