A
licenseA
qualityA
maintenanceEU AI Act Compliance - MCP server providing AI-powered tools and automation by MEOK AI Labs
11
12
MIT
employment-ai
by CSOAI-ORG
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@employment-airun bias detection on my hiring model"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Employment Ai
MEOK AI Labs — employment-ai MCP Server
MEOK AI Labs — employment-ai MCP Server
🚀 Quick Start
# Install via pip
pip install employment_ai
# Or install via Smithery
npx -y @smithery/cli@latest install employment-ai --client claudeRelated MCP server: attestix
✨ Features
MCP protocol compliant
Easy installation
Well-documented API
Production-ready
Active maintenance
📖 Documentation
🛡️ Compliance
This MCP server is built with EU AI Act compliance built-in:
✅ Article 9 — Risk Management System
✅ Article 13 — Transparency & Instructions for Use
✅ Article 15 — Bias Detection & Testing
✅ Article 26 — FRIA Support (where applicable)
✅ Article 50 — AI Content Watermarking (where applicable)
Need help getting compliant? Book a free 15-min diagnostic →
🏢 Enterprise
Need custom development, SLA guarantees, or white-label deployment?
Pro: $99/mo — Full MCP suite + EU AI Act tracking
Enterprise: $499/mo — Custom dev + SLA + Dedicated support
View Pricing → | Contact Sales →
🤝 Part of the MEOK Ecosystem
This server is part of the MEOK AI Labs ecosystem — 300+ MCP servers for sovereign AI governance.
Domain | Purpose |
EU AI Act compliance marketplace | |
AI safety & monitoring | |
Sovereign AI platform | |
Legacy modernization |
📜 License
MIT © CSOAI-ORG
Available Tools
2 toolshiring_ai_complianceB
Assess regulatory compliance for AI-based hiring, recruitment, and selection systems. Covers NYC Local Law 144, EEOC guidance, EU AI Act employment provisions, bias auditing, and candidate rights.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| system_name | Yes | Name of the hiring AI system | |
| ai_function | Yes | AI function (resume screening, video interview analysis, candidate ranking, skills assessment) | |
| data_inputs | Yes | Data inputs used (resumes, video, assessments, social media, etc.) | |
| jurisdiction | Yes | Operating jurisdiction (US/NYC, EU, UK, etc.) |
TDQS
B3.4/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must cover behavioral traits. It only lists covered regulations but does not disclose side effects (e.g., read-only), auth needs, or processing details (e.g., returns a report).
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?
Two concise sentences: first states core purpose, second lists covered areas. No redundant information. Front-loaded with key action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema; description does not hint at return value or format. Lacks behavioral details for a moderately complex tool with multiple regulations. With no annotations, more context is needed.
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% with clear descriptions. Description adds context about compliance assessment but no additional parameter-level meaning beyond schema. 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?
Description clearly states it assesses regulatory compliance for AI-based hiring systems, listing specific regulations (NYC Law 144, EEOC, EU AI Act). Distinguishes from sibling tool workplace_surveillance_compliance by focusing on hiring/recruitment.
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?
Description implies use for hiring AI compliance but does not explicitly state when to use vs. alternatives, nor provides exclusions or when-not-to-use guidance. No prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
workplace_surveillance_complianceA
Assess compliance for AI-based workplace monitoring and surveillance systems. Covers EU AI Act prohibitions, employee rights, proportionality, and Platform Workers Directive.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| system_name | Yes | Name of the workplace monitoring system | |
| monitoring_type | Yes | Type of monitoring (productivity, email, keystrokes, video, emotion, location) | |
| data_collected | Yes | Data collected about employees | |
| jurisdiction | Yes | Operating jurisdiction |
TDQS
A3.5/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full burden. It lists regulatory areas covered but does not disclose output format (e.g., report, score), behavioral traits (e.g., static analysis), or whether any additional context or system behavior is involved.
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 two concise sentences that front-load the purpose and scope. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description adequately explains the domain but omits what the tool returns or any behavioral limits. It differentiates from the sibling tool implicitly but not explicitly.
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 does not add extra meaning beyond the schema field descriptions; it provides context for the tool but not per-parameter enhancements.
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: to assess compliance for AI-based workplace monitoring systems. It specifies coverage of EU AI Act prohibitions, employee rights, proportionality, and Platform Workers Directive, which distinguishes it from the sibling tool 'hiring_ai_compliance'.
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 the tool should be used when evaluating workplace surveillance compliance but does not explicitly state when to use versus alternatives, nor provide any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
A3.6/5.0
Disambiguation5/5
The two tools cover distinct compliance areas—hiring vs. workplace surveillance—with no overlap in purpose or context.
Naming Consistency5/5
Both tools follow a consistent '[domain]_compliance' naming pattern with underscores, making the pattern predictable.
Tool Count3/5
Only two tools for a broad domain like employment AI feels thin, but they are focused on compliance assessment which partially justifies the count.
Completeness3/5
The tools cover hiring and surveillance compliance, but miss other employment AI areas like performance evaluation or promotion, leaving notable gaps.
Maintenance
ActivityInactive
ResponsivenessSyncing
Related MCP Connectors
AI governance MCP server for EU AI Act compliance and jurisdiction verification
AI legal compliance: contract review, risk scoring, EU/CN AI act, watermark check. 8 MCP tools.
EU AI Act uyum MCP sunucusu — risk sınıflandırma (madde atıflı), rol bazlı yükümlülük listesi…
EU compliance corpus across 8 frameworks (NIS2, DORA, AI Act, ISO 27001 + more) via MCP.
Related MCP Servers
- AlicenseNot gradedqualityBmaintenanceMCP server for compliance automation of AI agents, enabling EU AI Act compliance, verifiable credentials, and decentralized identity management with 47 tools across 9 modules.17Apache 2.0
- AlicenseAqualityDmaintenanceAn MCP server for AI economy infrastructure with built-in EU AI Act compliance, supporting risk management, transparency, and bias detection.10MIT
- AlicenseAqualityDmaintenanceMCP server for EU AI Act compliance, providing risk classification of AI features and Article 50 transparency notices.21Apache 2.0
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/CSOAI-ORG/employment-ai'
If you have feedback or need assistance with the MCP directory API, please join our Discord server