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competlab-mcp-server

get_ai_visibility_trend

Track how LLM brand perception changes over time. Get up to 200 data points for time-series analysis of AI visibility trends.

Instructions

Get AI Visibility trend data over time — track how LLM brand perception changes. Returns up to 200 data points. Without provider filter: returns pre-computed aggregate summaries across all LLMs. With provider filter (openai, claude, gemini): computes from raw per-provider results. Use this for time-series analysis; use get_ai_visibility_dashboard for the latest snapshot or get_ai_visibility_check_detail for a specific check. Read-only. Returns JSON array. Dates are ISO-8601 format.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateToNoEnd date in ISO-8601 format (e.g., 2026-03-15)
dateFromNoStart date in ISO-8601 format (e.g., 2026-01-01)
providerNoFilter by LLM provider. Omit for aggregate view across all providers
projectIdYesProject ID (from list_projects)
Behavior5/5

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

No annotations provided, but description covers key traits: read-only, returns up to 200 data points, JSON array, ISO-8601 dates, and explains aggregate vs per-provider computation. No contradictions.

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?

Four sentences, front-loaded with main purpose, each sentence adds necessary information without redundancy.

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?

Given no output schema, description covers return type, format, limits, and behavior for all parameter states, making it fully informative.

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?

With 100% schema coverage, baseline is 3. Description adds value by explaining the effect of provider filter on computation (aggregate vs raw) beyond schema descriptions.

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 the tool fetches trend data for AI visibility over time, tracking LLM brand perception. It distinguishes from siblings by specifying alternate tools for snapshot and check detail.

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?

Explicitly states 'Use this for time-series analysis' and names alternatives: get_ai_visibility_dashboard for latest snapshot, get_ai_visibility_check_detail for specific check. Also explains behavior with/without provider filter.

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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