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FlowFresh SA fruit reference data

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get_specs
Read-onlyIdempotent

Cultivars, origin regions, grade classes, official size codes/minimum sizes, carton reporting units and pack codes for a product, each labelled Fact or Inference with sources. Traded count ranges and some cartons are pending.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
product_idYesProduct id from list_products

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds real behavioral context that those annotations cannot express: every value is labelled Fact or Inference with sources, and traded count ranges plus some cartons are still pending, warning the agent that the payload is partially incomplete.

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?

Two sentences with no filler: the data inventory comes first and the data-quality caveat is placed last where it belongs. The opening is a verbless noun fragment, which makes it slightly harder to parse than it needs to be, but every clause carries information.

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 single-parameter read tool with no output schema, the description goes unusually far by naming the return fields and flagging incomplete data. The remaining gap is the relationship to siblings, particularly that product_id must come from list_products, which the description leaves to the schema.

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?

There is one parameter with 100% schema description coverage ('Product id from list_products'), so the schema already carries the semantics. The description's 'for a product' merely confirms the scope and adds no format, validation, or sourcing detail beyond 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 enumerates exactly what the tool returns for a product (cultivars, origin regions, grade classes, size codes, carton units, pack codes), which is concrete and far beyond a restatement of the name. It is framed as a bare noun list with no verb, and it draws no boundary against siblings like list_products or get_season, so it stops short of a 5.

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 phrase 'for a product' implies the tool is a per-product lookup, but there is no explicit when-to-use, no prerequisite statement (e.g. obtain product_id via list_products first), and no mention of when another sibling is preferable. An agent must infer everything about routing and sequencing.

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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