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competlab

competlab-mcp-server

get_ai_visibility_check_detail

Retrieve detailed AI visibility data for a past check, including per-competitor rankings, mention rates, AI Visibility Scores, and per-provider results from OpenAI, Claude, and Gemini.

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
checkIdYesCheck ID (from get_ai_visibility_history)
projectIdYesProject ID (from list_projects)
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It only states 'Read-only' and 'Returns JSON object', missing details like auth requirements, rate limits, or whether it modifies state. The note about same structure as another tool is helpful but insufficient.

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 concise with 4 sentences, front-loaded with purpose and outputs. 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?

Given no output schema, the description outlines the key returned data (rankings, scores, per-provider) and references another tool's data structure for clarity. With only 2 parameters, it covers the essential context.

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%, but the description adds context beyond the schema by stating 'Uses checkId (not runId)' and re-emphasizing where to get the IDs. This adds meaning for proper invocation.

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 'Get full detail for a specific AI Visibility check' with specific outputs (rankings, rates, scores, per-provider). It distinguishes from sibling get_ai_visibility_dashboard by noting '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?

The description explicitly tells where to get checkId values (from get_ai_visibility_history) and notes it uses checkId not runId. It states it is read-only. However, it does not explicitly state when to use this versus alternatives or when not to.

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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