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competlab-mcp-server

get_ai_visibility_history

Get paginated history of AI Visibility checks with completion timestamps. Each check runs a 3-prompt cycle across 3 LLMs.

Instructions

Get paginated history of AI Visibility checks with completion timestamps. Note: uses checkId (not runId) — AI Visibility has a different data model where each check is one 3-prompt x 3-LLM query cycle. Use this to browse past checks; retrieve full detail with get_ai_visibility_check_detail, or use get_ai_visibility_trend for aggregate time-series. Read-only. Returns paginated JSON array with pagination.hasMore flag.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number (1-indexed, default: 1)
limitNoItems per page (default: 20, max: 100)
projectIdYesProject ID (from list_projects)
Behavior4/5

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

The description states it is 'Read-only' and describes the return format as a 'paginated JSON array with pagination.hasMore flag.' It also highlights the unique data model. With no annotations provided, the description carries the full burden and adequately covers key behavioral aspects, though it could mention authentication or error handling.

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 only three sentences, each adding distinct value: purpose, data model note, usage guidance, and return format. No redundancy or filler.

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?

For a paginated browsing tool with no output schema, the description adequately describes the return format (paginated JSON array with hasMore flag) and mentions completion timestamps. It could include more detail on each item’s structure, but the sibling tool get_ai_visibility_check_detail handles full detail, so this is sufficient.

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 coverage is 100%, so the schema already documents all three parameters (page, limit, projectId) with constraints. The description does not add significant semantic detail beyond reinforcing pagination. Baseline 3 is appropriate as per guidelines.

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 explicitly states 'Get paginated history of AI Visibility checks with completion timestamps,' providing a specific verb and resource. It also clarifies the unique data model (each check is a 3-prompt x 3-LLM cycle) and distinguishes from siblings like get_ai_visibility_check_detail and get_ai_visibility_trend.

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?

The description explicitly states when to use this tool ('browse past checks') and when to use alternatives ('retrieve full detail with get_ai_visibility_check_detail, or use get_ai_visibility_trend for aggregate time-series'). It also warns about the checkId vs runId distinction, providing clear guidance.

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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