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zaboura

Vertica MCP Server

by zaboura

generate_health_dashboard

Create a consolidated health dashboard for Vertica Analytics Databases to monitor system performance and identify optimization opportunities in compact, detailed, or JSON formats.

Instructions

Generate consolidated health dashboard with controlled output. Args: ctx: The context object. output_format: The format of the dashboard (default: compact, detailed, json). Returns: A dictionary containing the health dashboard.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
output_formatNocompact
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'controlled output,' which hints at some behavioral trait, but doesn't elaborate on what this means (e.g., rate limits, permissions, or side effects). For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves beyond basic functionality.

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?

The description is front-loaded with the core purpose, followed by structured sections for args and returns. It's efficient with no wasted sentences, though the 'ctx' parameter lacks explanation, which slightly reduces clarity. Overall, it's appropriately sized and well-organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (generating a dashboard), lack of annotations, no output schema, and incomplete parameter documentation, the description is adequate but has clear gaps. It covers the basic purpose and some parameter details but misses behavioral context and full parameter semantics, making it minimally viable but not fully 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?

The description lists 'output_format' as a parameter with possible values ('compact, detailed, json'), which adds meaning beyond the input schema's 0% coverage. However, it doesn't explain the 'ctx' parameter at all, leaving it undocumented. With 1 parameter total and partial coverage in the description, this meets the baseline for minimal viability but doesn't fully compensate for the schema gap.

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?

The description clearly states the tool's purpose: 'Generate consolidated health dashboard with controlled output.' It specifies the verb ('generate') and resource ('health dashboard'), and the 'consolidated' modifier adds useful context. However, it doesn't explicitly differentiate this from sibling tools like 'analyze_system_performance' or 'database_status', which might offer overlapping functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, appropriate contexts, or exclusions, and there's no comparison to sibling tools like 'analyze_system_performance' or 'database_status' that might serve similar purposes. The agent must infer usage from the purpose alone.

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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