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thein-art

mcp-server-peecai

by thein-art

Brand Visibility Report

get_brands_report
Read-onlyIdempotent

Fetch a brand analytics report including visibility, share of voice, sentiment, mention count, and ranking. Filter by date, brand, or dimensions to analyze performance in AI-generated answers.

Instructions

Get brand analytics report per brand. Metrics: visibility (visibility_count/visibility_total), share_of_voice (0-1), mention_count, sentiment (0-100 scale, 50=neutral), position (avg rank when mentioned, lower=better). Returns up to limit results (default: 100). Use brand_id shortcut or filters array for server-side filtering. Supports date filtering and dimensional breakdowns. Without date filters, returns data across all available dates. Empty results may indicate the project has no report data for the given time range or filters — try a broader date range or fewer filters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idNoProject ID (uses PEECAI_PROJECT_ID env if omitted). Call list_projects to find IDs.
start_dateNoStart date (YYYY-MM-DD). Omit for no lower bound.
end_dateNoEnd date (YYYY-MM-DD). Omit for no upper bound.
dimensionsNoBreakdown dimensions. Each adds a grouping level to results: prompt_id (by search prompt), model_id (by AI model), model_channel_id (by model channel, e.g. openai-0/perplexity-0), tag_id (by category tag), topic_id (by topic group), date (by date), country_code (by country), chat_id (by individual chat). Multiple dimensions can be combined.
brand_idNoConvenience filter for a single brand (converted to server-side filter). Use list_brands to find IDs.
filtersNoServer-side filters. Multiple filters are AND'd together.
limitNoMax results (1-10000, default: 100)
offsetNoResults to skip
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, indicating safe read operation. Description adds beyond annotations: default date behavior (all available dates), empty result explanation, limit default/max. No contradiction.

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

Conciseness4/5

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

Description is a single focused paragraph that front-loads purpose and key metrics, then covers parameters and troubleshooting. Every sentence adds value, no redundancy. Could be slightly more structured with bullet points but still concise.

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 8 parameters, no output schema, and complexity, description covers key aspects: metrics, filtering, dimensions, default date range, limit/max, and error handling. Slightly misses offset pagination behavior but overall nearly complete.

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 description coverage is 100% (all parameters documented). Description adds significant value by explaining metrics (visibility, share_of_voice, sentiment scale, position interpretation) and dimension meanings (e.g., prompt_id by search prompt, model_id by AI model). This goes beyond schema definitions.

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

Purpose4/5

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

Description clearly states it retrieves brand analytics reports per brand with specific metrics. It's specific and uses clear verb-resource combination. However, it doesn't explicitly differentiate from sibling reporting tools like get_domains_report or get_urls_report, though the name implies brand focus.

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?

Description provides guidance on using brand_id shortcut vs filters array, references list_projects and list_brands for ID lookup, and explains empty results behavior with suggestions to broaden date range or reduce filters. Lacks explicit when-not-to-use compared to alternatives.

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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