gapup-mcp

retail_media_esg_compliance

Read-onlyIdempotent

Audits retail media networks for ESG compliance by analyzing ad placements, tracking cookies, and verifying ethical advertising standards. Designed for ad_revenue_ops teams to ensure GDPR and sustainability compliance across digital retail platforms. Accepts domain lists or network identifiers as input and returns structured compliance reports with warnings and source references. Requires async:true to avoid timeout errors.

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.
`domains`	No	List of retail media network domains to audit
`checkESG`	No	Enable ESG advertising standards compliance check
`checkGDPR`	No	Enable GDPR cookie tracking compliance check
`networkIds`	No	List of retail media network identifiers

Output Schema

TableJSON Schema

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

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds value by disclosing the asynchronous behavior requirement (async:true) and the output format (structured compliance reports with warnings and source references). This goes beyond what annotations provide, 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 sentences long with no extraneous information. The first sentence clearly defines the purpose, the second adds context (target users), and the third covers inputs, outputs, and a critical usage note about async. It is well-structured and front-loaded.

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 the presence of an output schema and annotations, the description covers the main aspects: input types, output format (compliance reports with warnings/references), and the async requirement. However, it could have elaborated on how to use the async mechanism (e.g., polling with job_result) or mentioned any rate limits or data volume concerns.

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?

The input schema has 100% description coverage for all 5 parameters. The description paraphrases 'domains' and 'networkIds' as inputs but adds no new detail about data types, constraints, or usage beyond what the schema already provides. The statement 'Requires async:true' is slightly misleading as async is optional (no required parameter).

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?

The description clearly states the tool audits retail media networks for ESG compliance by analyzing ad placements and tracking cookies. It specifies the target users (ad_revenue_ops) and compliance areas (GDPR, sustainability). However, it does not explicitly differentiate from sibling ESG tools like 'esg_audit_multi' or 'action_plan_esg', which could lead to confusion.

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 context for usage (ensuring GDPR and sustainability compliance for retail media networks) and includes an important note about requiring async:true to avoid timeouts. However, it does not specify when NOT to use this tool or mention alternative tools for similar tasks.

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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