Returns the full list of agent-discoverable surfaces Elephant implements: llms.txt, Schema.org Organization + Product blocks, MCP servers, A2A Agent Cards, UCP merchant metadata, agent-directory registrations, and citation-seeding.
| Name | Required | Description | Default |
|---|---|---|---|
| include_status | No | If true, includes which surfaces Elephant has shipped on its own domain (proof of practice). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It does not disclose read-only behavior, rate limits, or other traits beyond listing surfaces.
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?
Single sentence, highly concise, front-loaded with key action and resource.
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?
Lacks detail on return format for a tool with no output schema. Adequate for a simple list but could be more complete.
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 coverage is 100% and description adds no extra meaning to the 'include_status' parameter beyond what the schema already provides.
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?
Description clearly states it returns the list of agent-discoverable surfaces, with specific examples. Distinguishes from sibling tools like assess_fit or get_offerings.
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?
No explicit when to use or alternatives, but the purpose is clear enough to infer usage. Sibling tool names indicate different functions, so context is implied.
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 targets a distinct function: assessing fit, listing surfaces, offering details, proof points, transparency metrics, and requesting audits. No overlap in purpose.
All tool names follow a consistent verb_noun pattern using snake_case (e.g., assess_fit, get_offerings, request_audit), making them predictable and easy to differentiate.
With 6 tools, the server is well-scoped for its domain of LLM SEO services, covering all key interactions without being bloated or sparse.
The tool surface covers prospecting, information retrieval, and action (request_audit), but lacks a mechanism for checking audit status or cancellation, which is a minor gap.