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CSOAI-ORG

Explainability Report MCP

generate_model_card

Generate an EU AI Act compliant model card documenting your AI model's purpose, training data, and transparency details for regulatory compliance.

Instructions

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.

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. 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_nameYes
purposeYes
training_dataNo
api_keyNo
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.

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