gapup-mcp

observability_metric_anomaly_detector

Read-onlyIdempotent

As a CTO, quickly identify anomalous cloud metrics (CPU, latency, memory) by comparing your infrastructure against AWS public benchmarks and CVE-linked hardware risks. Input your observed metrics (e.g., CPU utilization, request latency) and receive a risk assessment with potential root causes. Ideal for performance troubleshooting, security hardening, and capacity planning. Keywords: cloud observability, anomaly detection, CVE hardware risks, AWS benchmark comparison.

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.
`region`	No
`metricType`	Yes
`instanceType`	No
`observedValue`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`cveRisks`	No
`warnings`	No
`anomalyScore`	No
`benchmarkValue`	No
`deviationPercent`	No

TDQS

A4.2/5.0

Behavior4/5

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

Annotations indicate readOnlyHint=true and idempotentHint=true. The description adds context by mentioning comparison with benchmarks and CVE risks, implying a non-destructive analysis. 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?

Three sentences: first states core purpose, second describes input/output, third lists use cases. No wasted words; front-loaded with key information.

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 output schema exists (context signals), the description adequately covers the tool's functionality without needing to detail return values. Mentions risk assessment and root causes, providing sufficient completeness for an agent.

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 20% (low). The description adds meaning by giving examples for metricType (CPU, latency, memory) and observedValue (e.g., CPU utilization). However, it does not explain region or instanceType parameters.

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 identifies anomalous cloud metrics (CPU, latency, memory) by comparing against AWS benchmarks and CVE risks. The verb 'identify' and specific resource scope distinguish it from sibling observability tools like 'observability_log_pattern_miner'.

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 provides context for use cases: 'Ideal for performance troubleshooting, security hardening, and capacity planning.' It does not explicitly state when not to use or name alternatives, but the context is clear.

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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