gapup-mcp

retail_media_attribution_bridge

Read-onlyIdempotent

Provides unified attribution insights for retail media and programmatic campaigns by analyzing MMM signals from FreeWheel Marketplace and Common Crawl. Designed for ad revenue operations teams to bridge cross-channel performance gaps. Accepts campaign IDs, date ranges, and channel filters as input. Returns structured attribution data with source provenance and confidence scores.

Input Schema

TableJSON Schema

Name	Required	Description
`async`	No	If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
`endDate`	Yes	End date for attribution window (YYYY-MM-DD)
`channels`	No	Channels to include in analysis
`startDate`	Yes	Start date for attribution window (YYYY-MM-DD)
`campaignIds`	Yes	List of campaign identifiers to analyze
`confidenceThreshold`	No	Minimum confidence score for included signals

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`warnings`	No
`attribution`	No

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint, openWorldHint, and idempotentHint, but the description adds valuable behavioral context: it analyzes MMM signals from FreeWheel Marketplace and Common Crawl, and returns structured data with source provenance and confidence scores. This goes beyond the annotations without contradiction.

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 three concise sentences: first states the core function and data sources, second identifies the target audience, third outlines inputs and outputs. Every sentence serves a purpose with no wasted words. Front-loaded with the most critical 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 existence of an output schema and comprehensive annotations, the description sufficiently covers the tool's purpose, input parameters, behavioral context, and output nature. It provides a complete understanding for an AI agent to select and invoke the tool correctly.

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 description coverage is 100%, so the baseline is 3. The description mentions campaign IDs, date ranges, and channel filters but does not add new meaning beyond what the schema already provides for each parameter. No per-parameter elaboration is given in the description.

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's purpose as providing unified attribution insights for retail media and programmatic campaigns by analyzing specific data sources. It identifies the target audience (ad revenue operations teams) and the core function (bridging cross-channel performance gaps). This is a specific verb+resource combination that distinguishes the tool from generic attribution tools.

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 provides clear context on when to use the tool ('bridge cross-channel performance gaps' for ad revenue operations) but does not explicitly contrast with sibling tools like 'programmatic_attribution_calibrator' or 'retail_media_esg_compliance'. It gives a strong usage scenario without exclusions, meeting the 'clear context' criteria.

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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TDQS

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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