get_fix_planThe prioritised fix list from the latest deep scan: the specific changes that move this business up in AI answers, highest leverage first.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already signals a safe read operation, and the description adds meaningful context beyond that: the data comes from the latest deep scan, is prioritised, and is ordered by highest leverage first. This gives agents useful expectations about the result's nature and ordering.
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, tightly written sentence that front-loads the core identity ('prioritised fix list') and then adds the key explanatory detail. Every word contributes value with no redundancy.
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 simple, zero-parameter, read-only tool with no output schema, this description is complete: it states the source, the content, and the ordering. An agent can correctly decide when to call it and what to expect in return.
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, so there is no parameter semantics gap for the description to fill. The empty input 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 clearly identifies the resource as the prioritised fix list from the latest deep scan and explains what it contains: specific changes to improve AI answers. It is distinguishable from the sibling get_* analytics tools, though it lacks an explicit retrieval verb like 'returns' or 'gets'.
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 implies this is used after a deep scan, but it does not explicitly state when to use this tool versus any alternatives. There is no when-not-to-use guidance or comparison to sibling tools.
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.