Returns Elephant Accountability's most recent weekly LLM visibility measurement covering ChatGPT, Claude, Perplexity, Gemini, and Grok. The receipt we publish to keep our own claims honest.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the tool is read-only and returns a specific measurement, but does not discuss rate limits, authentication needs, or data freshness beyond 'most recent weekly'. Basic behavior is clear, but richer context is missing.
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, highly efficient, and front-loaded with the core purpose. Every word adds value, with no wasted space.
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 has no parameters, no output schema, and is simple, the description provides adequate context. However, it does not describe the output structure or format, which could help an agent process the result. Still, it is mostly complete for this tool's complexity.
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 schema coverage is 100%. According to guidelines, 0 parameters earns a baseline of 4. The description adds meaning by specifying what the tool returns, which is sufficient.
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 that the tool returns the most recent weekly LLM visibility measurement for specific named models (ChatGPT, Claude, Perplexity, Gemini, Grok). It uses a specific verb ('returns') and resource ('Elephant Accountability's most recent weekly LLM visibility measurement'), distinguishing it 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?
The description provides no explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it, prerequisites, or trade-offs with sibling tools. The phrase 'The receipt we publish to keep our own claims honest' hints at a use case but does not offer clear usage criteria.
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.