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by app.linkgpt
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Change the tracked questions

set_tracked_questions
Destructive

Replaces the questions each AI visibility audit asks (1-10) with the list the user approved. Save it exactly as approved: suggest changes before the user says yes, not while saving. Takes effect from the next audit; past results keep their questions. Returns the before and after, and a changeId for undo_change.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "questions": "<questions>"
    },
    "name": "set_tracked_questions"
  }
}

Input Schema

NameRequiredDescriptionDefault
reasonNoWhy this helps, in a sentence, citing the data you used. Shown in the Action Center.
questionsYesThe full new list, in order, as the user approved it.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the destructive-mutation annotations, it discloses full-list replacement, preservation of past audit results, and effect only from the next audit. It also documents the before/after response and changeId used for undo, which is especially valuable because there is no output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the operation and scope, then efficiently covers approval timing, effective date, historical behavior, and return values. Every sentence provides actionable information without repetition or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter destructive configuration update, the definition covers the prerequisite approval, replacement semantics, timing, historical impact, response contents, and undo mechanism. Together with the fully described schema and annotations, it leaves no material invocation ambiguity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and both parameters are already well documented, including the 1-10 item bounds, ordering, approval requirement, and the reason's display location. The description reinforces the approved-list semantics and connects the questions to audits, but adds little parameter-specific information beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence gives a specific verb and resource: it "Replaces the questions each AI visibility audit asks" with an approved list. The replacement wording and audit context clearly distinguish this mutation from get_tracked_questions and run_ai_visibility_audit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says to invoke this only with "the list the user approved" and to suggest changes before approval rather than while saving. It also supplies the surrounding workflow by explaining when changes take effect and identifying undo_change as the route for reversal.

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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