Latest effective facts per subject+metric from the versioned fact table — the bitemporal as_of cutoff is exposed; an absent store is the honest empty view.
| Name | Required | Description | Default |
|---|---|---|---|
| as_of | No | Optional bitemporal cutoff (RFC 3339). | |
| domain | Yes | Measured domain («checker», «benchmark»). |
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, so the safety profile is already known. The description adds meaningful behavioral context: the as_of cutoff is exposed, and an absent store returns an honest empty view rather than an error. This helps an agent anticipate edge-case behavior beyond what annotations supply.
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 a single sentence, front-loaded with the core purpose, and every clause adds information about behavior or semantics. There is no filler or redundant restatement of the tool name.
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?
With only two parameters, one of them optional, and a readOnly annotation, the description covers the main concerns: what is returned, the temporal cutoff, and the empty-store case. Without an output schema, it leaves some return-shape detail implicit, but the tool is simple enough that this is a minor gap.
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 already documents both parameters. The description reinforces the role of as_of through the bitemporal wording, but it does not add substantial new parameter-level meaning 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 clearly identifies the resource ('versioned fact table') and the specific result ('latest effective facts per subject+metric'). It does not explicitly name a sibling differentiator, but the bitemporal and fact-table framing makes its role distinct from the other listed tools.
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?
Usage is implied rather than stated: an agent can infer this is the tool for retrieving current or as-of effective facts, especially when bitemporal cutoff matters. However, there is no explicit guidance about when to prefer an alternative tool or exclude this one.
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.