gapup-mcp

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

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.
`maxTests`	No	Maximum number of adversarial tests to run
`modelOutput`	Yes	The AI model's output or decision to be stress-tested
`adversarialDataset`	No	Optional custom adversarial inputs to test
`sensitivityThreshold`	No	Threshold for flagging high-risk adversarial examples

Output Schema

TableJSON Schema

Name	Required	Description
`status`	Yes
`sources`	No
`warnings`	No
`riskScore`	No	Normalized risk score from adversarial testing
`failureModes`	No
`adversarialExamples`	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, openWorldHint, and idempotentHint. The description adds behavioral details: it is asynchronous, requires the async flag, and outputs status, warnings, and source references. It aligns with annotations (no contradiction) and adds context about timeout avoidance.

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, front-loaded with purpose, then persona, async requirement, and outputs. Every sentence adds value without redundancy or verbosity.

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 tool's complexity (5 params, async, NIST RMF), the description covers purpose, usage hint, and output types. The existence of an output schema (has output schema: true) reduces the burden. It could elaborate on NIST RMF protocols, but overall it is sufficiently complete.

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 all 5 parameters with descriptions (100% coverage). The description repeats the modelOutput parameter ('accepts model outputs or decision boundaries') and mentions the async flag requirement, but adds little beyond schema for maxTests, adversarialDataset, and sensitivityThreshold. Baseline 3 is appropriate.

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 tool evaluates AI model resilience against adversarial inputs following NIST AI RMF protocols, specifying it accepts model outputs or decision boundaries and returns structured risk scores, failure modes, and adversarial examples. This distinguishes it from siblings like jailbreak_attempt_detector or safety_guardrail_breach_analyzer.

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 explicitly notes 'Requires async:true to avoid timeout errors', guiding usage for performance. It also mentions it is 'designed for security and compliance personas', providing context. However, it lacks explicit when-not-to-use or alternative tool references, though siblings exist.

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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