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mfgcalcs

by com.mfgcalcs
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Answer manufacturing questions from committed data; deterministic, no LLM. Missing subjects and product ties ask for clarification. Calculators collect required inputs, retain corrections via context, and run when complete; defaults appear only in explicit worked examples. Statistics name period, frequency, scope and measure. detail: answer is compact, evidence adds scoped evidence and calculator guidance, full is default. Pass returned context for follow-ups. Shared by MFGChat, MFGSearch and /api/answer. Cite resolved sources. Try: {"query":"OEE","detail":"evidence"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesA full question, e.g. 'tariff on fasteners' or 'diesel price'
detailNoResponse depth. `answer` is compact; `evidence` adds scoped evidence and adjacent facts; `full` preserves the compatibility-default forensic payload. Defaults to `full`.full
contextNoOptional. The `context` string from a previous ask response. Pass it to ask a follow-up: 'and what about vietnam' or 'why did it go up' then resolve against the earlier subject instead of being answered on their own.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / context / maxLength
      Added value: +1024
    • addedInput schema / properties / detail
      Added value: +{
+  "default": "full",
+  "description": "Response depth. `answer` is compact; `evidence` adds scoped evidence and adjacent facts; `full` preserves the compatibility-default forensic payload. Defaults to `full`.",
+  "enum": [
+    "answer",
+    "evidence",
+    "full"
+  ],
+  "type": "string"
+}
  2. Changed1 schema field changed
    • addedInput schema / properties / context
      Added value: +{
+  "description": "Optional. The `context` string from a previous ask response. Pass it to ask a follow-up: 'and what about vietnam' or 'why did it go up' then resolve against the earlier subject instead of being answered on their own.",
+  "type": "string"
+}
  3. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +false
  4. First observed

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses deterministic/no-LLM execution, clarification on missing subjects/product ties, calculator behavior with `context` retention, statistics requirements, source citation, and follow-up handling. It does not explicitly address read-only safety or failure modes, but covers a substantial range of runtime behaviors.

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 compact and front-loaded with the core purpose, then moves into behavioral details and parameter guidance. Some phrasing is cryptic ('Statistics name period, frequency, scope and measure'), but every sentence adds information and the JSON example grounds usage effectively.

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 tool with no output schema, the description covers query format, clarification behavior, calculator interaction, detail levels, follow-up context, and source citation. It does not describe the response payload shape or error behavior, but overall it provides enough actionable information for an agent to invoke the tool correctly.

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 100%, so the baseline is 3. The description adds value by explaining follow-up resolution ('resolve against the earlier subject') and providing a concrete example query, but mostly restates the `detail` semantics already present in the input 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 opens with a specific verb and resource: 'Answer manufacturing questions from committed data; deterministic, no LLM.' This clearly states the core function and distinguishes it from LLM-based question answering. However, it does not explicitly contrast with sibling tools like lookup_tariff or optimize_sourcing, so it misses full differentiation.

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 clear usage context: pass returned `context` for follow-ups, choose between `detail` levels, and includes a concrete example query. It states the tool is shared by MFGChat, MFGSearch and `/api/answer`, but does not name alternative tools or exclusions, so guidance is contextual rather than comparative.

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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