get_visibilityThe latest AI Visibility and AI Readiness scores (each /100) for areyoufoundbyai.com, with the previous week's scores, the separate off-site Footprint score, and the subscores (citability, E-E-A-T, technical, schema, platform compose Readiness; Footprint sits beside it).
| Name | Required | Description | Default |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark readOnlyHint=true, and the description does not contradict this. It adds useful behavioral context by describing the return payload in detail: latest scores, previous week's scores, off-site Footprint score, and named subscores, which goes beyond the minimal annotation.
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 one dense sentence that front-loads the core scores and then lists the remaining components in a parenthetical. Every phrase carries information, though the long embedded list makes it slightly less scannable than a structured multi-sentence version.
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 zero-parameter read-only metrics tool with no output schema, the description is complete: it specifies the domain, score types, scale, comparison period, Footprint component, and all subscores. An agent has enough information to call the tool and interpret its results.
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 empty input schema fully documents this. With no parameters to describe, the baseline of 4 applies; no additional parameter semantics are needed.
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 retrieves AI Visibility and AI Readiness scores for areyoufoundbyai.com, each on a /100 scale, and enumerates the exact data components returned. The metric-specific language distinguishes it from sibling tools like get_ai_traffic or get_benchmark without requiring schema inspection.
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 intended use is implied by the title and description: call this tool when you need current visibility and readiness scores. However, it does not explicitly state when to prefer this tool over siblings or provide any exclusions/alternatives, leaving some inference to the agent.
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.
Most tools have clearly distinct objects: answers, trajectories, citations, mentions, traffic, scores, and briefs. A few pairs like get_citation_sources vs get_source_profile and get_rivals vs get_share_of_voice overlap thematically, but their descriptions clarify different granularity and purpose.
All 16 tools follow an identical get_ + snake_case noun phrase pattern, such as get_agent_view, get_share_of_voice, and get_question_trajectories. This makes the tool set highly predictable and easy to navigate.
At 16 tools, the set sits just above the ideal 3-15 range, but each tool addresses a distinct facet of AI visibility monitoring. The count feels justified rather than bloated, though it is slightly heavy for a read-only monitor.
The suite covers the full read-only monitoring lifecycle: visibility scores, raw answers, question histories, competitor comparisons, cited sources, mentions, traffic, benchmarks, action plans, personas, and content briefs. There are no obvious dead ends or missing core operations for the stated purpose.