gapup-mcp

safety_violation_incident_logger

Read-onlyIdempotent

Logs AI safety violations for compliance reporting, targeting risk management personas. Accepts incident details such as violation type, severity, description, and timestamp. Returns structured data with compliance categorization based on NIST AI RMF guidelines. Ideal for automated incident tracking and regulatory reporting workflows.

Input Schema

TableJSON Schema

Name	Required	Description
`async`	No	If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
`metadata`	No
`severity`	Yes
`timestamp`	Yes
`description`	Yes
`violationType`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`warnings`	No
`incidentId`	No
`nistReference`	No
`complianceCategory`	No

TDQS

C2.7/5.0

Behavior1/5

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

The description states 'Logs AI safety violations,' implying a write operation, but annotations declare readOnlyHint=true, creating a direct contradiction. This severely undermines transparency. Beyond the contradiction, no behavioral traits like auth needs or side effects are disclosed.

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 concise with three sentences, front-loading the main purpose. It is efficient but could benefit from structured formatting (e.g., bullet points) for key details.

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?

The tool has an output schema and mentions returning structured data with NIST categorization, which is helpful. However, the contradiction between description and annotations creates confusion about actual behavior. Given many safety-related siblings, more disambiguating context is needed for completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 17%, and the description merely lists the required parameters (violationType, severity, description, timestamp) without adding semantic details like format constraints or allowed values beyond what enums provide. It fails to compensate for low schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool logs AI safety violations for compliance reporting and mentions targeting personas. It includes specific verb (logs) and resource (AI safety violations). However, it does not explicitly differentiate from similar sibling tools like bias_amplification_tracker or safety_guardrail_breach_analyzer, lacking direct sibling differentiation.

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 notes it is ideal for automated incident tracking and regulatory reporting workflows, providing some context. However, it offers no explicit guidance on when not to use it or alternatives to consider. The usage context is implied but not fully explicit.

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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TDQS

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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