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Log a batch

log_batch

Use this when the user has made a batch of a formula and wants it recorded: total weight, date, label and notes, optionally at a weaker strength, and optionally deducting what it used from stock. Works from the working copy or a version.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeNo
notesNo
madeAtNoISO date, default today.
formulaYesThe formula: its id (from list_formulas) or its exact name.
versionNo
totalGramsYes
deductStockNo
concentrationPctNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare a non-readonly, non-idempotent, non-destructive write. The description adds genuinely new behavior beyond that: the optional stock deduction side effect, the weaker-strength option, and that it can run against the working copy or a version. It still omits permission requirements and what success looks like.

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?

A single well-formed sentence with the usage trigger front-loaded and no filler. It is dense but every clause corresponds to a distinct capability, so nothing reads as padding.

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?

For an 8-parameter mutation tool with no output schema, the description covers the intent and the notable side effect but stops short of the full picture: no return/confirmation behavior, no auth or validation notes, and no explicit routing to list_batches for verification.

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 description coverage is only 25%, so the description must carry the load. It conceptually covers most parameters (totalGrams, madeAt, notes, concentrationPct, deductStock, version) in prose, but never maps them to parameter names, leaves 'code' vs 'label' ambiguous, and gives no formats or bounds.

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?

States a specific verb (log/record) and resource (a batch of a formula), and enumerates the payload it records (weight, date, label, notes, strength, stock deduction). It's clearly distinguishable from the read-side sibling list_batches, though it never names that sibling.

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?

Opens with an explicit trigger: 'Use this when the user has made a batch of a formula and wants it recorded.' That's a clear when-to-use condition, but there is no when-not guidance and no alternative named (e.g., list_batches for reviewing, or update_formula for edits).

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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