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Dweeb1578

Marketing Analytics MCP Server

by Dweeb1578

traffic_report_datapack

Compute weekly traffic data pack across Google Search Console, Bing, GA4, and Google Ads, handling API lags and isolating failures to warnings. Provides pre-computed cross-references for LLM analysis.

Instructions

Complete, deterministic data pack for the weekly traffic report (READ-ONLY).

Folds the traffic skill's GSC / Bing / GA4 / Google Ads batches plus LLM-referral gathering into one Python call: computes the Mon–Sun target + previous weeks (GSC gets its ~3-day lag), pulls every metric, and pre-computes the cross-references the skill used to ask the LLM to derive — the "Bing leads Google" candidate list and any emerging AI-surface referrer not yet in the curated list.

Each platform batch is isolated so one API failure degrades to a warnings entry instead of aborting the pack.

Args: week_ending: Anchor date YYYY-MM-DD (default: today). weeks_back: Completed weeks back to target (default: 1 = last week).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
weeks_backNo
week_endingNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It explicitly states READ-ONLY, deterministic behavior, failure handling with warnings, and details about the computation (Monday-Sunday target, GSC lag, pre-computed cross-references). This provides rich behavioral context.

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?

The description is well-structured with a front-loaded summary, but it is somewhat lengthy. While every sentence adds value, it could be slightly more concise without losing information.

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 the tool's complexity (aggregating multiple sources), the description covers what it does, how it handles failures, and what it computes. It also mentions outputs (pre-computed cross-references, warnings). No output schema is provided, but the description sufficiently explains the return value context.

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

Parameters5/5

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

Schema coverage is 0%, meaning the input schema properties lack descriptions. The description fully compensates by explaining both parameters: week_ending (anchor date in YYYY-MM-DD format) and weeks_back (number of completed weeks), along with their defaults.

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 is a 'Complete, deterministic data pack for the weekly traffic report (READ-ONLY)'. It specifies the verb 'data pack' and the resource 'weekly traffic report'. This distinguishes it from sibling tools that are individual platform queries (e.g., ads_campaigns, gsc_search_analytics).

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 implies usage for aggregating data for a weekly traffic report, but it does not explicitly state when not to use or provide alternatives. However, the context of sibling tools makes it clear that this is an aggregate alternative to individual platform calls.

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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