Competitors AI names instead
get_rivalsThe competitor names the answer engines actually gave in the latest scan when they did not name this business.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
get_rivalsThe competitor names the answer engines actually gave in the latest scan when they did not name this business.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already covers the tool's safety profile, so the description does not need to restate that. It does add useful context about the data scope (latest scan) and the condition under which these competitor names appear, but it does not describe return format or other behavioral details.
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 a single sentence with no filler and gets to the core concept quickly. It is slightly awkward and the title 'Competitors AI names instead' adds no clarity, but the description itself remains compact.
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 tool, the description sufficiently explains what data will be returned: competitor names from answer engines under a specific condition. It does not describe output structure, but no output schema is provided and the statement is adequate for selection and invocation.
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 schema is fully described by its empty object, so there are no parameter semantics for the description to clarify. The baseline of 4 applies because no parameter documentation burden exists.
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 resource (competitor names given by answer engines) and the specific condition (when they did not name this business). It distinguishes itself from siblings like get_mentions, which would cover mentions of the business itself, though it lacks an explicit verb and the phrasing is somewhat awkward.
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 guidance is provided about when to use this tool instead of related siblings such as get_share_of_voice, get_mentions, or get_answers. The intended use is implied by the name and description, but there is no explicit context or alternative routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.