Returns the Universal Language research briefing — structured content about the formally derivable geometric pattern language. Useful for answering questions about UL, AI alignment through ontological context, or UQPL.
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | Level of detail. 'summary' returns llms.txt (~2KB), 'full' returns llms-full.txt (~15KB). Default: summary. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description's verb 'Returns' implies a read-only retrieval with no side effects, but it does not explicitly disclose behavior beyond that, such as default depth or whether content is static. However, as a simple retrieval tool, this is adequate.
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 concise sentences cover purpose and usage with no filler; the description is front-loaded with the action verb.
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 lacks an output schema, but the description indicates structured content and the depth parameter provides detail options. It's sufficiently complete for a simple retrieval tool, though it doesn't elaborate on the briefing's structure.
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 fully describes the depth parameter with enum values and defaults, so the description need not add more; the 100% schema coverage sets a baseline of 3, and the description adds no extra parameter semantics.
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 states the tool returns a specific briefing about Universal Language, with a distinct resource type. It identifies the content domain and distinguishes from sibling tool names by focusing on a unique research briefing.
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 explicitly mentions when to use it—for questions about UL, AI alignment, or UQPL—but does not discuss exclusions or alternative tools, yielding a clear but incomplete guideline.
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.
Each tool retrieves a distinct type of information: agent card, context documents, service catalog, and research briefing. No overlap in purpose, and the descriptions clearly differentiate the content domains.
All tool names follow the exact same get_<noun> pattern, making the naming perfectly consistent and predictable.
With 4 tools, the server is tightly scoped for its informational purpose. Each tool covers a core aspect of the agency, and there are no redundant or extraneous tools.
The server covers the main informational needs about the agency, its services, context framework, and underlying language research. A minor gap is the lack of an interaction or order tool, but for an informational MCP the surface is largely complete.