What can you do with this server?

This server provides AI explainability, transparency, and EU AI Act compliance tools for analyzing and documenting AI systems. * Quick Scan: Instantly assess any AI system's transparency and explainability by providing a description — no API key required (free tier: 10/day). * Generate Model Card: Create an EU AI Act compliant model card with structured transparency information, including model purpose and training data details. * Explain Decision: Generate human-readable explanations for AI decisions (e.g., loan denials), optionally with contributing factors like credit score or income. * Transparency Audit: Assess an AI system against EU AI Act Article 13 transparency requirements, producing a structured compliance report. * Create Impact Assessment: Generate a DPIA/AIIA template for an AI system, tailored to specific affected groups (e.g., employees, customers, public). All tools are read-only, stateless, and idempotent. Coverage includes EU AI Act articles on Risk Management, Transparency, Bias Detection, FRIA, and AI Content Watermarking. Free tier allows 10 calls/day; Pro/Enterprise tiers (requiring a MEOK API key) are unlimited.

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Explainability Report MCP

by CSOAI-ORG

Overview Schema Related Servers Score Discussions

Python

MCP Scorecard: 74/100

mcp-name: io.github.CSOAI-ORG/explainability-report-mcp

Explainability Report MCP

MEOK AI Labs EU AI Act License PyPI

By [MEOK AI Labs](https://meok

By MEOK AI Labs | The only MCP server for AI explainability and transparency reports.

🚀 Quick Start

# Install via pip
pip install explainability_report_mcp

# Or install via Smithery
npx -y @smithery/cli@latest install explainability-report-mcp --client claude

Related MCP server: Consciousness Engine MCP

✨ 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
councilof.ai	EU AI Act compliance marketplace
safetyof.ai	AI safety & monitoring
meok.ai	Sovereign AI platform
cobolbridge.ai	Legacy modernization

📜 License

MIT © CSOAI-ORG

Pairs with MEOK Governance Suite

Build something that touches users? You need compliance. MEOK ships 38 governance MCPs that drop in alongside this tool — EU AI Act, DORA, NIS2, CRA, GDPR, ISO 42001, FDA SaMD, MDR, Basel, MiFID II, MiCA, COPPA, and more.

# One-shot install of the governance pack
npx meok-setup --pack governance

Free tier: 10 calls/day per MCP. Pro tier (£79/mo): unlimited + cryptographically signed compliance attestations your auditor verifies independently.

→ Full catalogue: councilof.ai/catalogue → MEOK AI Labs: meok.ai

💸 Try MEOK in 30 seconds — instant buy ladder

Tier	Price	What you get	Stripe
Smoke test	£1	Signed sample MCP-Hardening report + Article 50 PDF	https://buy.stripe.com/aFa7sNcgAdQS0ZT1Uc8k91t
Quick Kit	£9	EU AI Act Article 50 implementation guide (C2PA + EU-Icon)	https://buy.stripe.com/aFa7sNcgAdQS0ZT1Uc8k91t
Founder Call	£29	30-min 1-on-1 with the founder	https://buy.stripe.com/aFa7sNcgAdQS0ZT1Uc8k91t

Refundable. UK Stripe — VAT-clean. Builds on the 81-MCP MEOK fleet. Verify any signed report at https://meok.ai/verify.

Configuration

Add to your claude_desktop_config.json (Claude Desktop) or your MCP client config:

{
  "mcpServers": {
    "explainability-report-mcp": {
      "command": "uvx",
      "args": ["explainability-report-mcp"]
    }
  }
}

Or: pip install explainability-report-mcp then run the explainability-report-mcp command (stdio transport).

Examples

Once configured, ask your assistant, for example:

"Use quick_scan to …"
"Use generate_model_card to …"
"Use explain_decision to …"

Available Tools

5 tools

create_impact_assessmentA

Generate a DPIA/AIIA (AI Impact Assessment) template for an AI system.

Args: system_name: Name of the AI system. affected_groups: Comma-separated list of affected groups (e.g. "employees,customers,public"). Leave empty for generic template. api_key: Optional MEOK API key for pro tier.

Behavior: This tool generates structured output without modifying external systems. Output is deterministic for identical inputs. No side effects. Free tier: 10/day rate limit. Pro tier: unlimited. No authentication required for basic usage.

When to use: Use this tool when you need structured analysis or classification of inputs against established frameworks or standards.

When NOT to use: Not suitable for real-time production decision-making without human review of results.

ParametersJSON Schema

Name	Required	Description	Default
`system_name`	Yes
`affected_groups`	No
`api_key`	No

TDQS

A4.8/5.0

Behavior5/5

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

The description explicitly states the tool generates structured output without modifying external systems, has deterministic output for identical inputs, and no side effects. It also notes no authentication required for basic usage. This is comprehensive behavioral disclosure.

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 well-structured with clear sections and bullet points, making it easy to read. While informative, it could be slightly more concise (e.g., merging the behavior section lines), but overall it earns its length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, usage, parameters, and behavior. It mentions output is a template but does not specify the exact structure or format. Given no output schema, a bit more detail on output would improve completeness, but it is still adequate.

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 0% schema description coverage, the description explains each parameter: system_name as 'Name of the AI system', affected_groups with example and default behavior, api_key as optional for pro tier. This fully compensates for missing schema 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?

The description clearly states it generates a DPIA/AIIA template for an AI system. It names the specific output type and lists parameters. It distinguishes from sibling tools (explain_decision, generate_model_card, quick_scan, transparency_audit) by focusing on impact assessment.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit 'When to use' and 'When NOT to use' sections, guiding the agent on appropriate contexts. It also mentions rate limits (free tier: 10/day, pro tier: unlimited) and authentication requirements, giving clear usage boundaries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

explain_decisionA

Generate a human-readable explanation of an AI decision.

Args: decision: The AI decision or prediction to explain (e.g. "Loan application denied"). factors: Comma-separated contributing factors (e.g. "credit_score:620,income:35000,debt_ratio:0.45"). Leave empty for generic guidance. api_key: Optional MEOK API key for pro tier.

When to use: Use this tool when you need structured analysis or classification of inputs against established frameworks or standards.

When NOT to use: Not suitable for real-time production decision-making without human review of results.

ParametersJSON Schema

Name	Required	Description	Default
`decision`	Yes
`factors`	No
`api_key`	No

TDQS

A4.7/5.0

Behavior5/5

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

Description fully compensates for missing annotations by detailing behavior: no side effects, deterministic output, rate limits (free/pro tiers), and authentication needs. Contradiction is false.

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?

Well-structured: summary line, Args, Behavior, usage sections. Every sentence adds value; no wasted words. Appropriate length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers purpose, parameters, behavior, and usage guidelines. Lacks explicit output format description; mentions 'structured output' but could clarify whether it's text or JSON. No output schema provided.

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?

With 0% schema coverage, description explains each parameter: 'decision' (the AI decision), 'factors' (comma-separated, optional for generic), and 'api_key' (optional for pro tier). Adds critical meaning beyond schema titles and types.

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 states 'Generate a human-readable explanation of an AI decision', which is a specific verb and resource. It clearly distinguishes from siblings like create_impact_assessment or generate_model_card.

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?

Includes explicit 'When to use' and 'When NOT to use' sections, providing context for when the tool is appropriate. However, the 'When to use' description is somewhat generic and does not directly compare to sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

generate_model_cardA

Generate an EU AI Act compliant model card with structured transparency information.

Args: model_name: Name of the AI model or system. purpose: Description of the model's intended purpose. training_data: Description of training data used (leave empty if not available). api_key: Optional MEOK API key for pro tier.

When to use: Use this tool when you need structured analysis or classification of inputs against established frameworks or standards.

When NOT to use: Not suitable for real-time production decision-making without human review of results. Behavioral Transparency: - Side Effects: This tool is read-only and produces no side effects. It does not modify any external state, databases, or files. All output is computed in-memory and returned directly to the caller. - Authentication: No authentication required for basic usage. Pro/Enterprise tiers require a valid MEOK API key passed via the MEOK_API_KEY environment variable. - Rate Limits: Free tier: 10 calls/day. Pro tier: unlimited. Rate limit headers are included in responses (X-RateLimit-Remaining, X-RateLimit-Reset). - Error Handling: Returns structured error objects with 'error' key on failure. Never raises unhandled exceptions. Invalid inputs return descriptive validation errors. - Idempotency: Fully idempotent — calling with the same inputs always produces the same output. Safe to retry on timeout or transient failure. - Data Privacy: No input data is stored, logged, or transmitted to external services. All processing happens locally within the MCP server process.

ParametersJSON Schema

Name	Required	Description	Default
`model_name`	Yes
`purpose`	Yes
`training_data`	No
`api_key`	No

TDQS

A4.5/5.0

Behavior5/5

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

With no annotations provided, the description fully compensates by detailing side effects (read-only, no side effects), authentication (basic free, optional API key for pro), rate limits (10/day free), error handling (structured errors), idempotency, and data privacy. This thorough coverage exceeds typical expectations.

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 well-structured with sections (Args, Behavior, When to use, Behavioral Transparency) and front-loaded with purpose. However, it is verbose, with some repetition between the 'Behavior' section and the bullet points under 'Behavioral Transparency,' slightly reducing conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description could elaborate on the model card's output structure beyond 'structured transparency information.' Nonetheless, it covers parameters, usage, behavior, and error handling thoroughly, making it mostly complete for an MCP tool with moderate complexity.

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?

Despite 0% schema description coverage, the description's 'Args' section adds meaning beyond titles: 'leave empty if not available' for training_data, 'Optional MEOK API key for pro tier' for api_key. However, model_name and purpose descriptions are minimal; more detail (e.g., format constraints) would elevate it further.

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 'Generate an EU AI Act compliant model card with structured transparency information,' specifying a concrete verb and resource. It differentiates from sibling tools like 'create_impact_assessment' and 'transparency_audit' by emphasizing structured analysis against frameworks.

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?

Explicit 'When to use' and 'When NOT to use' sections provide clear context: use for structured analysis, avoid for real-time decisions without human review. However, it does not directly name sibling alternatives for those use cases, missing a small opportunity for even better guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

quick_scanA

Describe an AI system -> instant transparency and explainability assessment. No API key required.

Behavior: This tool is read-only and stateless — it produces analysis output without modifying any external systems, databases, or files. Safe to call repeatedly with identical inputs (idempotent). Free tier: 10/day rate limit. Pro tier: unlimited. No authentication required for basic usage.

When to use: Use this tool when you need structured analysis or classification of inputs against established frameworks or standards.

ParametersJSON Schema

Name	Required	Description	Default
`description`	Yes

TDQS

A4.6/5.0

Behavior5/5

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

With no annotations provided, the description carries the full burden and excels. It details side effects (read-only, stateless), authentication, rate limits, error handling, idempotency, and data privacy comprehensively.

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 well-structured with sections and front-loaded purpose. While thorough, it is somewhat verbose; every sentence earns its place, but brevity could be improved slightly.

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 simplicity (one parameter, no output schema), the description is remarkably complete. It covers usage, privacy, rate limits, error handling, and idempotency, leaving no significant gaps.

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?

The schema has one parameter 'description' with 0% coverage. The description adds context by stating the input should be a description of an AI system, but lacks additional details like format or examples.

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: 'Describe an AI system -> instant transparency and explainability assessment.' It uses a specific verb and resource, and distinguishes itself from siblings by emphasizing instant results and no API key required.

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 includes explicit 'When to use' and 'When NOT to use' sections, providing clear context. However, it does not explicitly name alternative tools from the sibling list for differentiation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

transparency_auditA

Assess an AI system against EU AI Act Article 13 transparency requirements.

Args: system_description: Detailed description of the AI system including its purpose, capabilities, data, and deployment context. api_key: Optional MEOK API key for pro tier.

When to use: Use this tool when you need structured analysis or classification of inputs against established frameworks or standards.

ParametersJSON Schema

Name	Required	Description	Default
`system_description`	Yes
`api_key`	No

TDQS

A4.7/5.0

Behavior5/5

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

Despite no annotations, the description contains a dedicated 'Behavioral Transparency' section that comprehensively covers side effects (none), authentication (none for basic, API key for pro), rate limits (10/day free, unlimited pro), error handling (structured errors), idempotency (fully idempotent), and data privacy (no storage/logging). This fully compensates for missing annotations.

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 longer but well-organized with clear headings (Args, Behaviors, When to use, Behavioral Transparency). Every section adds value, though some sentences could be combined without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers purpose, parameters, behavioral details, usage guidelines, and error handling comprehensively. Lacks explicit mention of return format or output fields, which would be ideal since no output schema exists. Minor gap given the tool's complexity.

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?

Schema coverage is 0% (no individual parameter descriptions in schema), but the description explains both parameters: 'system_description' is detailed in the Args block, and 'api_key' is mentioned as optional for pro tier. While not exhaustive, it adds meaningful context beyond raw schema.

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?

Clearly states the tool assesses an AI system against EU AI Act Article 13 transparency requirements. The verb 'assess' and resource are specific, and it distinct from siblings like 'explain_decision', which targets individual decisions, and 'generate_model_card', which creates a standard documentation format.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit 'When to use' and 'When NOT to use' sections. It recommends use for structured analysis against frameworks and explicitly warns against real-time production decision-making without human review, offering clear guidance on appropriate contexts.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

5 tool updatesv1.0.4May 8, 2026
- First observedcreate_impact_assessment
- First observedexplain_decision
- First observedgenerate_model_card
- First observedquick_scan
- First observedtransparency_audit

Install Server

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TDQS

A4.4/5.0

Disambiguation4/5

Each tool has a distinct purpose (impact assessment, decision explanation, model card, quick scan, transparency audit). However, quick_scan and transparency_audit both assess transparency, with descriptions that are somewhat overlapping, potentially causing confusion for an agent.

Naming Consistency3/5

Most tools use verb_noun pattern (create_, explain_, generate_), but quick_scan uses adjective_noun and transparency_audit is noun_noun, breaking consistency. The naming is readable but not uniform.

Tool Count5/5

With 5 tools covering key explainability tasks (impact assessment, decision explanation, model card, scan, audit), the count is well-suited for the server's purpose, not too many or too few.

Completeness4/5

The tool set covers major explainability report needs (DPIA, model cards, decision explanations, transparency audits). Minor gaps exist (e.g., no tool for bias detection or combined summary), but core capabilities are present.

Maintenance

ActivityStale

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

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Explainability Report MCP

🚀 Quick Start

✨ Features

📖 Documentation

🛡️ Compliance

🏢 Enterprise

🤝 Part of the MEOK Ecosystem

📜 License

Pairs with MEOK Governance Suite

💸 Try MEOK in 30 seconds — instant buy ladder

See also

Configuration

Examples

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

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