gapup-mcp

programmatic_brand_safety_auditor

Read-onlyIdempotent

Evaluates programmatic ad inventory for brand safety risks using IAB Tech Lab's standards and GDPR-compliant tracking methods. Designed for ad revenue operations teams to assess inventory quality before bidding. Inputs include domain, page URL, and optional contextual signals. Outputs a structured brand safety score with risk categorization and compliance warnings.

Input Schema

TableJSON Schema

Name	Required	Description
`url`	Yes	Full page URL being evaluated
`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.
`domain`	Yes	Root domain of the inventory (e.g., 'example.com')
`categories`	No	Optional IAB content categories for contextual analysis
`gdprConsent`	No	GDPR consent string (TCF v2.0)

Output Schema

TableJSON Schema

Name	Required	Description
`flags`	No
`score`	No	Brand safety score (0-100)
`status`	Yes
`sources`	No
`warnings`	No
`riskLevel`	No
`gdprCompliant`	No

TDQS

A4/5.0

Behavior4/5

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

Annotations already indicate readOnly, idempotent, and openWorld hints. The description adds context beyond annotations by specifying the evaluation standards (IAB, GDPR), the output structure (brand safety score, risk categorization, compliance warnings), and the optional contextual signals. No contradiction with annotations.

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 with no fluff. First sentence defines purpose and standards, second sentence targets users and timing, third sentence lists inputs and output. Efficient and well-structured.

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 tool has 5 parameters and an output schema exists, the description covers the main inputs and output structure adequately. It mentions standards and compliance. However, it omits the async behavior (relevant for the async parameter) and does not detail the output schema (but output schema exists, so not required). Overall, fairly complete for a moderately complex tool.

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 coverage is 100%, so baseline is 3. The description mentions domain, page URL (which maps to domain and url), and optional contextual signals (categories), but does not explain the async parameter or gdprConsent beyond stating GDPR-compliance. It adds some value but not fully compensating for missing details.

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 evaluates programmatic ad inventory for brand safety risks using IAB standards and GDPR-compliant methods. It specifies the target users (ad revenue operations teams) and the timing (before bidding), distinguishing it from sibling tools like privacy_compliance_audit or ugc_moderation_classifier.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage 'before bidding' but does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or scenarios where this tool should not be used. Given the abundance of sibling tools, more explicit guidance would be beneficial.

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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