gapup-mcp

model_behavior_drift_monitor

Read-onlyIdempotent

Monitors AI model output drift by comparing current model responses against MLCommons safety benchmarks. Designed for risk and compliance personas to detect behavioral deviations that may indicate safety or alignment issues. Accepts model outputs or identifiers and returns structured drift metrics with statistical significance. Sources data from MLCommons public benchmark APIs.

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.
`threshold`	No	Drift threshold for alerting
`currentOutputs`	No	Recent model outputs to analyze for drift
`baselineMetrics`	No
`modelIdentifier`	Yes	Unique identifier for the model being monitored

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`warnings`	No
`driftMetrics`	No

TDQS

A4/5.0

Behavior4/5

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

Annotations already indicate readOnly, openWorld, and idempotent hints. Description adds context about sourcing data from external MLCommons APIs, returning structured drift metrics with statistical significance, and accepting model outputs or identifiers. This goes beyond annotations but could mention latency implications of external API calls.

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?

Three sentences with no waste. First sentence states primary function, second adds persona and outcome, third describes inputs/outputs and data source. Key information is front-loaded and 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?

Given good annotations, high schema coverage, and presence of an output schema, the description covers purpose, inputs, output type, and data source. It does not mention async behavior or error handling, but these are in schema. Overall complete for a monitoring tool.

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 already provides detailed descriptions for all parameters (80% coverage), including async, threshold, currentOutputs, baselineMetrics, and modelIdentifier. Description does not add new parameter semantics beyond mentioning 'model outputs or identifiers' which aligns with schema. 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?

Description clearly states the tool monitors AI model output drift by comparing against MLCommons safety benchmarks, targeting risk and compliance personas. It distinguishes from siblings like bias_amplification_tracker or hallucination_confidence_meter by specifying the benchmark source and drift detection focus.

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?

Description implies usage for drift detection with MLCommons benchmarks and risk/compliance personas, but does not explicitly state when to use this tool versus alternatives like bias_amplification_tracker or safety_guardrail_breach_analyzer. No when-not or alternative scenarios are provided.

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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