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Factory Intelligence MCP Server

Factory Intelligence MCP Server

by lvshrd
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_quality_kpi

Compute yield percentage and defect rate for a specified time range to assess production quality.

Instructions

Computes quality metrics (Yield %, Defect Rate %) for a given time range.

Args:
    start_time: ISO 8601 string
    end_time: ISO 8601 string

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": {
      "start_time": "<start_time>",
      "end_time": "<end_time>"
    },
    "name": "get_quality_kpi"
  }
}

Input Schema

NameRequiredDescriptionDefault
end_timeYes
start_timeYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the full burden. It states metrics are 'computed', implying a read-only operation, but does not confirm safety, data freshness, or any constraints like time range limits.

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?

Extremely concise: two sentences for purpose then parameter list. Front-loaded with intent, no wasted words.

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?

Adequate for a simple KPI tool. Covers parameters and basic output metrics, but missing details on aggregation level, error handling for invalid ranges, and description of return structure (though output schema exists).

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?

Schema coverage is 0%, but the description adds specific format information for both parameters (ISO 8601 strings), which goes beyond the schema's type-only definition. However, it does not explain acceptable date ranges or timezone handling.

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 computes quality metrics (Yield %, Defect Rate %) for a given time range. The specific metrics differentiate it from sibling KPI tools (downtime, productivity, summary).

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?

Does not provide any guidance on when to use this tool versus alternatives. No mention of prerequisites, exclusions, or when not to use it.

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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