mcp-knowledge

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

A4.7/5.0

Behavior5/5

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

Description adds significant behavioral context beyond annotations: statelessness (no storage/logging), auto language detection, async mode, output details (severity, confidence, matched patterns, etc.), and no API key requirement. Annotations already declare readOnlyHint=true and idempotentHint=true, but description enriches transparency with operational details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is well-structured with a concise opening line, bulleted policy list, language enumeration, and output summary. It is front-loaded with key information. However, it is somewhat lengthy (several paragraphs), and some repetition (e.g., 'No API key required' appears twice). A slightly more condensed version could improve conciseness.

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 tool's complexity (5 parameters, output schema exists), the description covers all aspects: input requirements (content, optional lang, policies, async, content_type), behavior (stateless, no storage, async polling), and output (severity, confidence, etc.). The existence of an output schema reduces the need to detail return values, but the description already provides ample context.

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

Parameters5/5

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

Despite 100% schema coverage, description adds meaningful context: each policy is listed with examples (e.g., 'hate — hate speech, slurs, dehumanization (50+ terms × 12 languages)'), languages explicitly enumerated, async parameter explained with usage guidance, and content_type's effect on recommended_action heuristic described. This far exceeds the schema's brief descriptions.

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?

Description explicitly states it is a multi-language UGC content moderation tool that detects policy violations in text across 9 policies and 12 languages. The verb 'detects' combined with concrete policy list leaves no ambiguity about its function, and it clearly distinguishes itself from sibling tools by focusing on moderation.

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?

Description implies usage for social platforms, marketplaces, and comment systems by stating 'Multi-language UGC content moderation for marketplaces, social platforms and comment systems.' It also specifies that no external API calls are needed and no API key is required. However, it does not explicitly contrast with similar tools like 'jailbreak_attempt_detector' or provide when-not-to-use scenarios.

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.

ugc_moderation_classifier

Input Schema

Output Schema

TDQS

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TDQS

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