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charles-gentry

rb209-mcp

rb209_recommendation_calculate_nutrient_deficiency

Calculate nutrient deficiency from leaf analysis by specifying crop type, leaf sampling position, nutrient ID, and nutrient content to identify deficiency levels.

Instructions

GET /api/Recommendation/CalculateNutrientDeficiency/{cropTypeId}/{leafSamplingPosition}/{nutrientId}/{nutrientContent} — To get the nutrient deficiency result bases on leaf analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cropTypeIdYes
nutrientIdYesnutrient Id
nutrientContentYescontent of nutrient
leafSamplingPositionYesleaf sampling position

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.3.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of disclosing behavior, but it only says the tool 'gets' a result. The GET verb in the path implies read-only behavior, yet there is no mention of output format, error conditions, required context, or limitations—leaving significant behavioral ambiguity.

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 a single sentence with no filler. The endpoint path is included, which is useful for invocation, and the purpose statement is brief. It could be phrased more naturally, but it is efficiently compact.

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

Completeness2/5

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

There is no output schema and no annotations, so the description should explain what the nutrient deficiency result looks like and under what conditions it is meaningful. It does not, leaving an agent unable to anticipate the response shape or handle likely edge cases.

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 schema already describes three of four parameters (nutrientId, nutrientContent, leafSamplingPosition), and the description adds little beyond restating their names in the URL path. cropTypeId lacks a schema description and is not elaborated in the description either, but the parameter names are largely self-explanatory.

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 a specific verb ('get'), a specific resource ('nutrient deficiency result'), and the basis ('leaf analysis'). It is distinguishable from the sibling calculate_nutrient_offtake tool by the resource it targets, though it does not explicitly call out that difference.

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?

There is no guidance on when to use this tool versus alternatives such as rb209_recommendation_calculate_nutrient_offtake or the many lookup tools. The phrase 'based on leaf analysis' implies a context, but no when-to-use/when-not-to-use or prerequisite information is provided.

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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