meta.capabilities
The platform capability register with published per-client coverage — tool names on this server ARE the capability ids.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
The platform capability register with published per-client coverage — tool names on this server ARE the capability ids.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
The readOnlyHint annotation already establishes the safe read-only nature. The description adds value beyond that by explaining that the returned capability IDs correspond to tool names on this server and that coverage is per-client, which helps an agent interpret results.
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 entire description is a single concise sentence that front-loads the core concept and adds one clarifying clause about capability IDs. Every part of the sentence contributes meaningful information.
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 simple, read-only, no-parameter meta endpoint, the description is adequate: it explains the resource, the per-client scope, and how capability IDs relate to tool names. It stops short of detailing the exact response shape, but the lack of an output schema and the low complexity make this 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?
The tool has zero parameters and the schema coverage is 100%, so there is nothing for the description to add about parameters. The baseline for a no-parameter tool is 4, and the description appropriately says nothing misleading about arguments.
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 identifies the tool as a platform capability register with per-client coverage, and clarifies that tool names on the server serve as capability IDs. This provides a clear resource and distinguishes it from sibling meta-tools like meta.schemas and meta.tools, though it lacks an explicit verb like 'list' or 'get'.
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 implies that this tool is used to discover published capabilities and their per-client coverage, which is useful context. It does not explicitly state when to prefer this over meta.tools or meta.schemas, but the naming and emphasis on 'capability ids' give reasonable situational clarity.
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.