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mcp-knowledge

by io.github.getgapup

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
NameRequiredDescriptionDefault
asyncNoIf 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.
contextNoContextual information about the prompt or conversation
llmOutputYesRaw text output from LLM to analyze for guardrail breaches
severityThresholdNoMinimum severity score to report (0-10 scale)
includeMitigationsNoWhether to include mitigation recommendations

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
breachesNo
warningsNo
complianceScoreNo

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the description does not need to repeat those. It adds value by describing the output (compliance analysis with severity scoring and mitigation recommendations). No contradictions.

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 is three sentences, immediately states the purpose, and uses no unnecessary words. It is well-structured and efficient.

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, the description does not need to detail return values. It covers purpose, input, target user, and output summary. However, it omits mention of the async parameter and the context parameter, which are part of the input schema.

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

Parameters3/5

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

Schema coverage is 100%, so baseline is 3. The description adds minor context about input types (raw or structured) and output components, but it does not significantly deepen understanding beyond schema descriptions. The async and context parameters are not addressed in the description.

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 verb 'Analyzes', the resource 'LLM guardrail breaches', and the specific standard 'IEEE 7000 ethical compliance standards'. It distinguishes from sibling tools like jailbreak_attempt_detector by referencing a specific ethical framework.

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 specifies the target user ('risk persona') and acceptable input formats ('raw LLM responses or structured breach reports'), providing clear context. However, it lacks explicit guidance on when not to use or alternatives among the numerous sibling tools.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

Resources