Skip to main content
Glama

mcp-knowledge

by io.github.getgapup

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
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.
thresholdNoDrift threshold for alerting
currentOutputsNoRecent model outputs to analyze for drift
baselineMetricsNo
modelIdentifierYesUnique identifier for the model being monitored

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
warningsNo
driftMetricsNo

TDQS

A4/5.0
Behavior4/5

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

Annotations declare readOnlyHint, idempotentHint, and openWorldHint. Description adds that it compares current outputs to MLCommons benchmarks, returns structured drift metrics with statistical significance, and sources data from public APIs. This enriches behavioral context beyond annotations without contradiction.

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?

Four concise sentences with no redundancy. Front-loaded with the core function, followed by audience, input/output summary, and data source. Every 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?

Covers purpose, target users, inputs, outputs, and data source. Output schema documents return values. Misses guidance on the async parameter, which is present in the schema but not addressed in the description. Otherwise complete for a monitoring tool with annotations.

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 high (80%) with most parameters described. Description mentions 'model outputs or identifiers' (mapping to currentOutputs and modelIdentifier) and implicitly threshold, but does not elaborate on async or baselineMetrics. Adds some value but not significantly beyond the schema.

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?

Clearly states the tool monitors AI model output drift, compares against MLCommons safety benchmarks, and targets risk/compliance personas. Differentiates from siblings like bias_amplification_tracker or model_safety_certification_checker by specifying the drift focus and benchmark source.

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 context that it is for risk and compliance personas to detect safety/alignment issues, but does not explicitly state when to use this tool vs alternatives or when not to use it. No mention of exclusions or sibling comparisons.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All mcp-knowledge toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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