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lu-query

lu-query

by zademy
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

get_system_stats

Retrieve cumulative Oracle system statistics from V$SYSSTAT, including parse count, execute count, DB time, and physical reads/writes for database performance monitoring.

Instructions

Return cumulative system statistics from V$SYSSTAT (parse count, execute count, DB time, physical reads/writes, etc.).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_system_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.16.3

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses that values are 'cumulative' (versus deltas/rates) and names the underlying V$SYSSTAT view, which implies a read-only operation, but it says nothing about permissions, row volume, or snapshot semantics.

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?

A single front-loaded sentence with zero waste; the resource, its source, and representative metrics are all delivered immediately.

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?

For a zero-parameter read tool with no output schema, the description does the needed work by naming the source view and sampling the returned metrics. It would be stronger with a note on the volume/granularity of returned rows, but coverage is reasonable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so there is nothing for the description to disambiguate. Baseline 4 applies since no parameter semantics are needed.

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?

States a specific verb (Return) and resource (cumulative system statistics from V$SYSSTAT) and enumerates concrete metrics like parse count, execute count, DB time, and physical reads/writes. This makes the tool's scope evident, though it does not explicitly distinguish itself from sibling stats tools such as get_db_time_model or get_instance_info.

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 offers no when-to-use guidance, no prerequisites, and no comparison against alternatives in the crowded stats/info sibling set. An agent must infer that this is the instance-wide cumulative-stat source.

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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