Returns the full machine-readable service catalog including available services, pricing, and payment methods. Use this to understand what Archangel Agency offers to AI agents.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral transparency burden. It discloses the return content (services, pricing, payment methods) and implies a read-only operation via 'Returns', but does not elaborate on behavior such as authentication needs, rate limits, or output format details. This meets the minimum viable level.
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 two sentences, front-loaded with the main action and followed by a purposeful usage note. No wasted words, and it is appropriately concise for a simple get tool.
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?
Given the tool's simplicity (no parameters, no output schema, read-only nature), the description sufficiently covers its purpose and content. It could optionally mention that output is machine-readable, which it does, but lacks details on format or constraints, which are not critical for a get-only catalog.
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 no parameters, so the description has no parameter details to convey. The baseline for zero parameters is 4, and the description appropriately omits any parameter-related text.
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 tool's function: it returns the full service catalog with specific content (services, pricing, payment methods). This specific verb+resource combination distinguishes it from sibling tools like get_agent_info and get_context_jsons.
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 states when to use the tool ('Use this to understand what Archangel Agency offers to AI agents'), providing clear context. However, it doesn't explicitly mention alternatives or when not to use it, though sibling tools are sufficiently distinct.
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.