Compliance Fairness
compliance_fairness[security] Fairness proof (FNS-100-FairnessBound)
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| metric | Yes | ||
| model_id | Yes | ||
| dataset_id | Yes | ||
| protected_attrs | Yes |
compliance_fairness[security] Fairness proof (FNS-100-FairnessBound)
| Name | Required | Description | Default |
|---|---|---|---|
| metric | Yes | ||
| model_id | Yes | ||
| dataset_id | Yes | ||
| protected_attrs | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds only a '[security]' tag and a standard identifier, with no additional behavioral context such as what the proof contains, whether authentication is required, or what the result represents.
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 short phrase, which is concise but under-specified rather than efficient. It is front-loaded with a security tag and standard ID but omits the information needed to invoke the tool correctly.
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?
For a tool with four required parameters, no output schema, and a specialized compliance purpose, the description is far too thin. It does not explain what a fairness proof is, how inputs are used, or what the agent should expect in return.
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?
All four parameters are required and schema description coverage is 0%, but the description does not explain model_id, dataset_id, protected_attrs, or metric. It provides no semantic guidance for any parameter, leaving the agent with only bare schema names.
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 states 'Fairness proof' and references FNS-100-FairnessBound, which signals a fairness-proofing tool. However, 'proof' is ambiguous about whether it computes, generates, verifies, or retrieves a proof, and it does not distinguish this tool from the many other compliance_* siblings.
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?
There is no guidance on when to use this tool versus alternatives such as compliance_check, compliance_eu_ai_act, or compliance_transparency. The description gives no prerequisites, exclusions, or context that would help an agent select it correctly.
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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