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calculate_reconstitution_dose

Calculate precise reconstitution dosing for peptide research compounds: determine the volume to inject in U-100 syringes from peptide mass, added water, and desired dose.

Instructions

Calculate precise peptide reconstitution dosing architecture for U-100 syringes

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
water_mlYesBacteriostatic Water Added in milliliters (ml)
peptide_mgYesPeptide Vial Quantity in milligrams (mg)
desired_dose_mgYesDesired Dose in milligrams (mg)
Behavior2/5

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

With no annotations provided, the description carries the full burden for behavioral disclosure. It does not mention what the output format is (e.g., mL or syringe units), assumptions about U-100 concentration, or any rounding/limitations. This is a meaningful gap for a calculation tool.

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 and front-loaded with the verb 'Calculate'. However, the phrase 'dosing architecture' is jargon and could be simplified to 'dose' for clarity, slightly reducing conciseness.

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?

The tool is simple (3 numeric parameters, no output schema), but the description does not explain the return value or any calculation assumptions. With no output schema and no annotations, the description should carry more context, such as the result being in mL or units, but it is too sparse.

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 does not add extra meaning to the parameters (peptide_mg, water_ml, desired_dose_mg), but the schema already documents each parameter clearly. Mentioning U-100 syringes indirectly hints at the calculation context but not enough to raise the score.

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 clearly states the tool calculates peptide reconstitution dosing for U-100 syringes, using a specific verb ('calculate') and resource ('reconstitution dose'). It distinguishes itself from sibling tools that provide protocols or guides, though the phrase 'dosing architecture' is somewhat vague.

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?

No guidance is provided on when to use this tool versus alternatives, such as get_peptide_protocol or get_syringe_mechanics_guide. The description offers no context for selection, prerequisites, or exclusions.

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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