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competlab

competlab-mcp-server

get_ai_visibility_check_detail

Retrieve detailed AI visibility data for a past check, including competitor rankings, mention rates, and scores per provider (OpenAI, Claude, Gemini). Uses check ID from history.

Instructions

Get full detail for a specific AI Visibility check including per-competitor rankings, mention rates, AI Visibility Scores, and per-provider results (OpenAI, Claude, Gemini). Returns the same data structure as get_ai_visibility_dashboard but for a past point in time. Uses checkId (not runId) — get checkId values from get_ai_visibility_history. Read-only. Returns JSON object.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectIdYesProject ID (from list_projects)
checkIdYesCheck ID (from get_ai_visibility_history)

Implementation Reference

  • Zod schema for input validation: requires projectId and checkId (both 24-char hex ObjectId strings).
    parameters: z.object({
      projectId: objectId("Project ID (from list_projects)"),
      checkId: objectId("Check ID (from get_ai_visibility_history)"),
    }),
Behavior4/5

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

Since no annotations are provided, the description carries full burden. It discloses read-only behavior and indicates the return structure is identical to get_ai_visibility_dashboard. Does not mention rate limits or auth, but for a read-only tool this is adequate.

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?

Three concise sentences: first states what it returns, second clarifies timing and structure, third explains ID usage and read-only. No redundant information; every sentence adds value.

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

Completeness4/5

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

Tool has two parameters, no output schema, no annotations. Description explains purpose, ID source, and return structure by referencing a sibling tool. This is sufficient for a simple read operation, though output details are not fully elaborated.

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

Parameters4/5

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

Schema coverage is 100% with descriptions for both parameters. The description adds extra value by clarifying that checkId comes from get_ai_visibility_history and distinguishes it from runId, which is not in 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 description clearly states it gets full detail for a specific AI Visibility check, listing specific data components (per-competitor rankings, mention rates, scores, per-provider results). It distinguishes from the sibling get_ai_visibility_dashboard by noting it returns the same structure but for a past point in time.

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

Usage Guidelines4/5

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

Provides clear context: uses checkId (not runId), instructs to get checkId from get_ai_visibility_history, and states it is read-only. Does not explicitly state when not to use, but the purpose is specific enough to guide usage.

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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