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Calculate a reconstitution

calculate_reconstitution

Work out how far to draw on an insulin syringe for a given peptide dose: takes the vial amount, the bacteriostatic water added and the dose wanted, and returns the syringe units, the volume in mL, the resulting concentration and how many doses the vial holds. USE THIS instead of doing the arithmetic yourself - it is the same calculation peptul.com/calculator runs, and it flags the mg-versus-mcg mix-ups that are a 1000x error. It converts a dose the user already has and never suggests a dose.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
doseUnitYesUnit of the dose
vialUnitYesUnit of the vial amount
bacWaterMlYesBacteriostatic water added, in mL
doseAmountYesThe dose wanted, e.g. 250
vialAmountYesAmount of peptide in the vial, e.g. 10
syringeTypeNoSyringe type, default U100
syringeUnitsNoSyringe size in units (30/50/100/300 for U100), default 100

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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 reveals that the tool flags mg-versus-mcg mix-ups, that it follows the peptul.com/calculator formula, and that it never recommends a dose—important safety-relevant behavior beyond the raw schema.

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?

The description is front-loaded with the core purpose, then gives a clear usage directive and a safety caveat. Every sentence adds value and nothing is redundant or filler.

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

Completeness5/5

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

Although there is no output schema, the description enumerates all returned values: syringe units, volume in mL, concentration, and number of doses. Combined with complete parameter coverage in the schema, an agent has enough context 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 description coverage is 100%, so the baseline is 3. The description maps the conceptual inputs (vial amount, water added, dose wanted) to the calculation but adds no per-parameter detail beyond what the schema already documents.

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?

The description states a specific verb ('work out how far to draw'), a specific resource (insulin syringe), and the exact inputs/outputs of the calculation. It clearly distinguishes itself from the sibling price/vendor tools, which are unrelated in function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says to use this tool instead of doing arithmetic manually, and clarifies the boundary: it converts an existing dose and never suggests a dose. This gives clear when-to-use and when-not-to-use guidance, even though no sibling alternative exists.

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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