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mcp-knowledge

by io.github.getgapup

adversarial_input_stress_tester

Read-onlyIdempotent

An asynchronous risk assessment tool that evaluates AI model resilience against adversarial inputs following NIST AI Risk Management Framework (RMF) red-teaming protocols. Designed for security and compliance personas, it accepts model outputs or decision boundaries and returns structured risk scores, failure modes, and adversarial examples. Requires async:true to avoid timeout errors. Outputs include status, warnings, and source references.

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.
maxTestsNoMaximum number of adversarial tests to run
modelOutputYesThe AI model's output or decision to be stress-tested
adversarialDatasetNoOptional custom adversarial inputs to test
sensitivityThresholdNoThreshold for flagging high-risk adversarial examples

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
warningsNo
riskScoreNoNormalized risk score from adversarial testing
failureModesNo
adversarialExamplesNo

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already signal readOnly, openWorld, idempotent. Description adds async requirement and output types (status, warnings, references) but no further behavioral detail. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with purpose, then async requirement, then outputs. No redundancy, each sentence adds value.

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?

With output schema implied and good annotations, description covers main purpose, async nuance, and output types. Complex tool with 5 params, but coverage is sufficient.

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 covers 100% parameters with descriptions, so baseline 3 is appropriate. Description does not add significant parameter-specific meaning beyond schema.

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?

Clearly states it evaluates AI model resilience against adversarial inputs following NIST AI RMF protocols. Identifies target persona (security/compliance) and outputs (risk scores, failure modes). Lacks explicit sibling differentiation but specific enough.

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?

Provides one explicit guideline: requires async:true to avoid timeouts. No when-to-use vs. alternatives or exclusions. Adequate but minimal.

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