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

A4.4/5.0

Behavior5/5

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

Annotations already mark it read-only and idempotent, but the description goes further by disclosing that no external API calls are made, no API key is required, and the tool is stateless with no content stored or logged. It also details the output fields (severity, confidence, matched patterns, excerpt, recommended action, age appropriateness, signals), exceeding annotation coverage.

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?

The description is front-loaded with a concise summary, then uses bullet lists for policies and languages, making it scannable and well-organized. It is longer than some tool descriptions, but the enumerated policy details and language list are directly useful for an agent deciding invocation and parameter values, so each line earns its place.

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 (9 policies, 12 languages, multiple optional parameters) and the presence of a rich output schema, the description fully covers use cases, policies, languages, behavioral guarantees (stateless, no API key), and output highlights. The agent has enough context to select the tool and configure relevant parameters correctly.

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 the schema already documents all five parameters with clear descriptions and enums. The description adds context on the overall policy and language sets, but does not add new semantics beyond what the schema provides for individual parameters. Baseline 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 uses a specific verb ('detects policy violations') and names the exact resource (text content for UGC moderation across marketplaces, social platforms, comment systems). It clearly distinguishes itself from sibling tools by enumerating the 9 policies and 12 languages, making its scope unambiguous.

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 clearly states when to use it: for moderating user-generated content across marketplaces, social platforms, and comment systems. It does not explicitly name alternatives or exclusions, but the content scope and policy list provide clear context for when this tool is appropriate.

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

C2.4/5.0

Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

ugc_moderation_classifier

Input Schema

Output Schema

TDQS

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TDQS

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