gapup-mcp

ugc_moderation_classifier

Read-onlyIdempotent

Multi-language UGC content moderation for marketplaces, social platforms and comment systems. Detects policy violations in text content across 9 policies and 12 languages without external API calls.

Policies checked: • hate — hate speech, slurs, dehumanization (50+ terms × 12 languages) • sexual — explicit sexual content, pornography references, nudity solicitation • violence — threats, weapon references, graphic violence • self_harm — suicidal ideation, self-injury, eating disorder promotion • harassment — doxxing, stalking, cyberbullying, blackmail • scam — phishing, investment fraud, romance scam, lottery fraud • spam — bots, keyword stuffing, excessive caps, emoji storms, suspicious URLs • copyright — piracy, leaked content, serial keys, streaming fraud • minor_safety — grooming signals, CSAM references, minor + adult content combos

Languages: en / fr / de / es / it / pt / nl / zh / ja / ko / ar / ru (auto-detected)

Output includes severity (low/medium/high/severe), confidence (0-100), matched patterns, excerpt, recommended action, age appropriateness (adult/teen/child), and signals.

No API key required. Stateless — no content is stored or logged.

Input Schema

TableJSON Schema

Name	Required	Description
`lang`	No	Language override. If omitted, language is auto-detected.
`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.
`content`	Yes	Text content to moderate (comment, review, post, chat message).
`policies`	No	Policies to check. Default: all 9 policies.
`content_type`	No	Type of content. Affects recommended_action heuristic. Default: comment.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`signals`	Yes
`sources`	Yes
`violations`	Yes
`lang_detected`	Yes
`quality_score`	Yes
`age_appropriate`	Yes
`content_preview`	Yes
`policies_checked`	Yes
`recommended_action`	Yes

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds value by specifying statelessness and no logging/storage, and describes output fields like severity and confidence, which align with the 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 well-structured with bullet points for policies, languages, and output fields. It is front-loaded with the main purpose and concise, with no wasted sentences.

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?

Despite complexity (9 policies, 12 languages), the description covers all relevant aspects: purpose, policies, languages, output details, and key traits. It is complete for an agent to understand and use the tool.

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

Parameters4/5

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

Input schema covers 100% of parameters with clear descriptions. The description adds context about output fields (severity, confidence, matched patterns, etc.), complementing the schema without redundancy.

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: multi-language UGC content moderation detecting policy violations. It specifies the resource (text content), the action (detects violations), and distinguishes itself from siblings like jailbreak_attempt_detector or bias_amplification_tracker.

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 provides context about when to use the tool (any UGC moderation) and notes key traits (no API key, stateless). However, it lacks explicit guidance on when not to use it or alternative tools, leaving the agent to infer from sibling context.

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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