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., "@healthcare-ai audit my AI diagnostic assistant for EU AI Act compliance"
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.
MEOK AI Labs — healthcare-ai MCP Server
MEOK AI Labs — healthcare-ai MCP Server
# Install via pip
pip install healthcare_ai
# Or install via Smithery
npx -y @smithery/cli@latest install healthcare-ai --client claudeRelated MCP server: atlas_mcp
MCP protocol compliant
Easy installation
Well-documented API
Production-ready
Active maintenance
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 →
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
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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 |
MIT © CSOAI-ORG
Comprehensive clinical AI safety assessment covering bias, validation, human oversight, and EU AI Act obligations for healthcare AI systems. Provides CASA tier recommendation.
| Name | Required | Description | Default |
|---|---|---|---|
| system_name | Yes | Name of the clinical AI system | |
| clinical_domain | Yes | Clinical domain (e.g., radiology, pathology, cardiology, oncology, drug prescribing) | |
| decision_autonomy | Yes | Level of AI decision autonomy (e.g., advisory, decision support, autonomous, triage) | |
| patient_population | Yes | Target patient population (e.g., adult, pediatric, geriatric, specific demographics) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states what the tool covers but does not disclose behavioral traits such as whether it is read-only, any authorization needs, or side effects. For a computation tool, it is likely non-destructive, but this is not explicitly stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single focused sentence that front-loads the purpose and scope. It is concise without wasted words, though it could be slightly more structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and 4 required parameters, the description is vague about what is returned ('CASA tier recommendation') and does not explain the output format or provide context on CASA tiers. This leaves the agent uncertain about what to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 four parameters. The description adds no additional semantics for the parameters; it only says the assessment covers certain topics which are not parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it performs a clinical AI safety assessment covering bias, validation, human oversight, and EU AI Act, and outputs a CASA tier recommendation. However, it does not explicitly differentiate from sibling tools (eu_mdr_compliance, fda_ai_assessment, hipaa_ai_assessment), which would help an agent choose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for comprehensive safety assessment including EU AI Act, but does not specify when to use it versus sibling tools that cover specific regulations. No exclusions or alternatives are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Assess EU Medical Device Regulation (MDR 2017/745) compliance for AI-based medical devices. Determines MDR classification, conformity assessment route, and regulatory requirements.
| Name | Required | Description | Default |
|---|---|---|---|
| risk_class | Yes | Estimated risk class (Class I, IIa, IIb, III) or 'unknown' for classification assistance | |
| system_name | Yes | Name of the AI medical device | |
| intended_purpose | Yes | Intended purpose per MDR Article 2(12) | |
| device_description | Yes | Description of the device including AI/ML components |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral traits. It describes what the tool assesses but does not disclose output format, prerequisites, limitations, or any side effects. For a tool that determines regulatory requirements, missing details like 'generates a compliance report' or 'requires clinical evidence' reduce transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the tool's purpose, and contains no filler. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the tool's function but does not detail the output or any prerequisites. With no output schema, the agent cannot anticipate what the tool returns (e.g., classification text, structured report). Additional information about the response would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with each parameter described. The tool description adds meaning by referencing MDR Article 2(12) for intended_purpose and specifying 'including AI/ML components' for device_description, enriching the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool assesses EU MDR 2017/745 compliance for AI-based medical devices, specifying it determines classification, conformity assessment route, and regulatory requirements. This clearly distinguishes it from sibling tools focusing on clinical safety, FDA, and HIPAA.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for EU MDR compliance but does not explicitly state when to use this tool versus the sibling tools (e.g., fda_ai_assessment for US FDA). The sibling names are provided in context, but the description lacks explicit usage exclusions or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Evaluate FDA regulatory requirements for AI/ML-based Software as a Medical Device (SaMD). Determines device classification, regulatory pathway, and compliance requirements per FDA AI/ML Action Plan.
| Name | Required | Description | Default |
|---|---|---|---|
| system_name | Yes | Name of the AI medical device or SaMD | |
| intended_use | Yes | Intended use statement for the AI system | |
| ai_model_type | Yes | Type of AI model (e.g., static, adaptive, continuous learning, reinforcement learning) | |
| clinical_context | Yes | Clinical context (e.g., diagnosis, treatment, screening, monitoring) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so description carries full burden. The description only mentions the tool's function (evaluating requirements) and does not disclose behavioral traits such as whether it is read-only, if it accesses external databases, or any side effects. Minimal transparency beyond the core purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences front-load the verb 'Evaluate' and clearly convey the purpose without superfluous words. Structure is efficient and impactful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Description covers the main purpose and mentions outputs (classification, pathway, compliance). However, without an output schema, the description could be stronger by specifying the format of the assessment. It is mostly complete but lacks explicit output details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with each parameter having a clear description. The tool description does not add extra meaning beyond the schema; it only indirectly implies use of parameters through the functional description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it evaluates FDA regulatory requirements for AI/ML SaMD and determines classification, pathway, and compliance. This distinguishes it from sibling tools (clinical_ai_safety, eu_mdr_compliance, hipaa_ai_assessment) which focus on other regulatory frameworks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for FDA AI/ML SaMD assessment but does not explicitly state when to use versus alternatives or when not to use. No direct comparison to siblings, though domain specificity provides implicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Assess HIPAA compliance for AI systems processing Protected Health Information (PHI). Evaluates administrative, physical, and technical safeguards required for AI systems in healthcare settings.
| Name | Required | Description | Default |
|---|---|---|---|
| system_name | Yes | Name of the healthcare AI system | |
| phi_data_types | Yes | Types of PHI the system processes (e.g., diagnostic data, genomic data, medication records) | |
| system_description | Yes | Description of the AI system and its role in healthcare delivery | |
| deployment_environment | Yes | Where the system is deployed (e.g., cloud, on-premise, hybrid) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral disclosure. It indicates a non-destructive assessment (evaluate safeguards), but does not explicitly state that the tool is read-only, auth requirements, or side effects. The description is adequately transparent but lacks explicit confirmation of safety.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loading purpose in the first sentence and elaborating in the second. Every word adds value, no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 required parameters, no output schema, siblings), the description defines scope and evaluation areas but omits information about return value or output format. It is adequate but not fully complete for an agent to understand the full behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 adds context about evaluating administrative, physical, and technical safeguards, but does not enhance parameter meaning beyond the schema descriptions. Therefore, no extra value above baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: assessing HIPAA compliance for AI systems processing PHI. It uses a specific verb ('assess') and resource ('HIPAA compliance for AI systems'), and it distinguishes from siblings by focusing on HIPAA specifically, whereas siblings cover other regulatory domains.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for HIPAA compliance assessments but does not explicitly state when to use it over siblings like fda_ai_assessment or eu_mdr_compliance. No guidance on prerequisites or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool targets a distinct regulatory framework (general AI safety, EU MDR, FDA SaMD, HIPAA), with no overlap in purpose. An agent can easily differentiate them.
Names follow a pattern of domain/regulation plus topic, but vary between 'safety', 'compliance', and 'assessment' (e.g., clinical_ai_safety vs. eu_mdr_compliance vs. fda_ai_assessment). Slight inconsistency but still clear and predictable.
Four tools is well-scoped for a healthcare AI compliance server, covering major regulations without excess. Each tool serves a necessary role.
Covers core regulatory areas (safety, EU MDR, FDA, HIPAA), but misses some potentially relevant frameworks like GDPR or ISO standards. Minor gaps that don't severely hinder the agent.
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