get_question_trajectoriesEvery tracked buyer question with its measured history: how many of the 7 engines named the business on each measured day. This is where visibility is actually won or lost, question by question.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Read-only annotation already signals safety, and the description adds meaningful behavioral context: it returns a time-series-like history across measured days for all tracked buyer questions, counting engine mentions. It does not describe output formatting or pagination, but the absence of parameters and read-only nature keep this a minor gap.
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 sentences and the core definition is front-loaded. The second sentence is slightly rhetorical but reinforces the granularity and strategic relevance, so it mostly earns its place without adding hard operational 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?
Given zero parameters, a read-only annotation, and no nested schema, the description provides everything an agent needs to invoke this tool correctly and interpret what it represents. Nothing essential is missing.
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 coverage is 100%, so there is no parameter ambiguity. Baseline 4 applies because there is no parameter-usage burden for the agent to overcome.
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 names a specific resource (tracked buyer questions) and a precise metric (how many of the 7 engines named the business on each measured day). It also distinguishes itself from aggregate siblings like get_visibility and get_share_of_voice by emphasizing question-by-question breakdown.
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 clearly implies this is for per-question visibility history rather than aggregate metrics, and the closing sentence reinforces the granular context. However, it does not explicitly name sibling tools to avoid or state when not to use it, so it stops short of full alternative routing.
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.