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mcp-knowledge

by io.github.getgapup

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
NameRequiredDescriptionDefault
urlYesFull page URL being evaluated
asyncNoIf 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.
domainYesRoot domain of the inventory (e.g., 'example.com')
categoriesNoOptional IAB content categories for contextual analysis
gdprConsentNoGDPR consent string (TCF v2.0)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
flagsNo
scoreNoBrand safety score (0-100)
statusYes
sourcesNo
warningsNo
riskLevelNo
gdprCompliantNo

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint as true, indicating safety. The description adds value by mentioning GDPR-compliant methods and the output format (structured score, risk categorization, compliance warnings), which goes beyond 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 concise (3 sentences), front-loaded with the core purpose, audience, and input/output structure. Every sentence adds value with no redundancy.

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 full schema coverage and an output schema (implied), the description adequately covers what the tool does. It mentions the output structure and target use case. Minor omission: the async behavior is not mentioned in the description, but it is covered in the schema.

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 each parameter has a description in the schema. The tool description only lists inputs at a high level without adding new meaning beyond what's in the schema. Baseline of 3 is appropriate.

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, specifying standards like IAB Tech Lab and GDPR. It differentiates itself from sibling tools by focusing on brand safety, as opposed to other audit tools (e.g., privacy_compliance_audit).

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 identifies the target audience (ad revenue operations teams) and the use case (assessing inventory quality before bidding). It does not explicitly state when not to use or compare to alternatives, but the context is clear.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

Resources