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ApexMCP

ApexMCP

by mad-science-lab
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

apex_probe_stats

Computes min, mean, max, daily means, and day-over-day drift for one Apex probe by name or device ID over a chosen period.

Instructions

min/mean/max, daily means and day-over-day drift for one probe (name or device id).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "probe": "<probe>"
    },
    "name": "apex_probe_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
daysNo
probeYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
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 the computed aggregate behavior and daily granularity, but omits read-only status, time range behavior, and any data-source or permissions context.

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 lists the key metrics first with no filler. Every phrase contributes to identifying what the tool returns.

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?

The tool is low complexity and has an output schema, so return values need not be explained. However, with no annotations and 0% schema description coverage, the missing days parameter semantics leave a meaningful gap.

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

Parameters2/5

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

Schema coverage is 0%, so the description must compensate. It explains only the probe parameter by noting name or device id, while the days parameter is not mentioned at all and its default/behavior is left entirely to the schema.

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 names the resource (one probe) and the exact metrics returned (min/mean/max, daily means, day-over-day drift). It is clear but does not explicitly distinguish itself from the closely related sibling apex_probe_history.

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?

No when-to-use guidance or alternatives are provided. The parenthetical clarifies the probe identifier but does not tell an agent when this tool should be chosen over apex_probe_history or apex_get_probes.

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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