gapup-mcp

safety_guardrail_breach_analyzer

Read-onlyIdempotent

Analyzes potential LLM guardrail breaches against IEEE 7000 ethical compliance standards. Designed for risk persona to evaluate safety violations in AI outputs. Accepts raw LLM responses or structured breach reports, returns compliance analysis with severity scoring and mitigation recommendations.

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.
`context`	No	Contextual information about the prompt or conversation
`llmOutput`	Yes	Raw text output from LLM to analyze for guardrail breaches
`severityThreshold`	No	Minimum severity score to report (0-10 scale)
`includeMitigations`	No	Whether to include mitigation recommendations

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`breaches`	No
`warnings`	No
`complianceScore`	No

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is clear. The description adds valuable context beyond annotations: it specifies the analysis standard (IEEE 7000) and output content (severity scoring, mitigation recommendations). There is no contradiction with annotations.

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?

The description consists of three concise sentences, front-loading the core purpose. Every sentence adds essential information without redundancy.

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 the presence of an output schema and full schema coverage, the description adequately covers purpose, inputs, outputs, and target user. It lacks details on error handling or edge cases, but these are not critical for a well-annotated tool.

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 100%, baseline is 3. The description adds meaning by clarifying that 'llmOutput' accepts both raw text and structured reports, and linking 'severityThreshold' and 'includeMitigations' to the output components. This exceeds what the schema alone provides.

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 uses specific verbs ('Analyzes') and resources ('LLM guardrail breaches against IEEE 7000 ethical compliance standards'), clearly distinguishing it from siblings like 'safety_violation_incident_logger' (logs) and 'jailbreak_attempt_detector' (detects jailbreaks). It also specifies the output components ('compliance analysis with severity scoring and mitigation recommendations').

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 mentions the target persona ('risk persona') and input types ('raw LLM responses or structured breach reports'), but it does not explicitly state when to use this tool versus alternatives like 'ai_act_incident_response' or 'bias_amplification_tracker'. More precise exclusionary guidance would improve it.

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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