get_share_of_voiceYour share of AI-answer mentions across you and the competitors the engines name in your space, with the movement over the tracked window. Measured from real answers, not estimated.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, and the description adds meaningful behavioral context: it is measured from real answers, not estimated, and includes movement over the tracked window. This goes beyond the schema and helps the agent set expectations about data source and output shape, even though return format details are not spelled out.
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 tight sentences with no wasted words. The core metric is front-loaded, and the data-quality note ('Measured from real answers, not estimated') earns its place as a distinguishing detail.
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?
With no output schema, the description explains what the tool computes and that there is a trend component. The tracked window and exact competitor selection are left vague, but for a zero-parameter, read-only metric this is sufficient for an agent to invoke it correctly.
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 is empty, which is the baseline-4 case. There are no parameters to describe, and the description correctly does not invent parameter guidance. The schema already communicates that no arguments are required.
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 states a specific metric (share of AI-answer mentions), the scope (you and competitors the engines name in your space), and a temporal component (movement over the tracked window). This clearly distinguishes it from sibling tools like get_mentions (raw mentions) or get_rivals (competitor list).
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 alternative-routing guidance is given. The context implies it is for competitive share comparison, but it does not tell the agent when to prefer this over get_benchmark, get_mentions, or get_rivals. Usage is implied rather than explained.
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.