Skip to main content
Glama
Bitcraze26

pizza-mcp

by Bitcraze26
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Calcola dosi impasto pizza

calculate_dough
Read-onlyIdempotent

Calculates pizza dough ingredient amounts in grams—flour blend, water, salt, and yeast—using ball count, weight, hydration, and fermentation schedule.

Instructions

Calcola tutte le dosi dell'impasto della pizza con il metodo diretto: grammi di ogni farina, acqua, sale e lievito, con le percentuali del fornaio.

Parametri principali: numero e peso dei panetti, idratazione desiderata, una o più farine con le rispettive percentuali, ore e temperatura di lievitazione (a temperatura ambiente e/o in frigorifero) e tipo di lievito.

Il lievito viene stimato con un modello tempo/temperatura: percentuale di lievito fresco = 3,6 / ore equivalenti a 20 °C, con attività che raddoppia ogni 10 °C. Se conosci già la percentuale di lievito, puoi forzarla con yeast_percent. Le percentuali di farina, acqua, sale e lievito sono tutte sul peso della farina (farina totale = 100%).

Input Schema

NameRequiredDescriptionDefault
ballsYesNumero di panetti da preparare.
floursNoUna o più farine con la percentuale sul totale della farina. Default: un'unica farina al 100%.
room_hoursNoOre di lievitazione a temperatura ambiente (default 24; usa 0 se fai tutto in frigo). Per l'appretto dopo il frigo usa 1-2 ore.
yeast_typeNoTipo di lievito: fresh = fresco (default), dry = secco attivo, instant = istantaneo.
fridge_hoursNoOre di lievitazione in frigorifero (default 0, cioè nessun frigo). Se usi il frigo, aggiungi 1-2 ore a temperatura ambiente per l'appretto.
salt_percentNoSale in percentuale sul peso della farina (default 2,5). Il valore tipico è 2-3%.
ball_weight_gYesPeso di ciascun panetto in grammi (es. 250).
yeast_percentNoOpzionale: forza la percentuale di lievito (del tipo scelto) sul peso della farina, ignorando il modello tempo/temperatura.
hydration_percentNoIdratazione desiderata in percentuale sul peso della farina (default 62). Es. 62 per il 62%.
room_temperature_cNoTemperatura ambiente in °C (default 20).
fridge_temperature_cNoTemperatura del frigorifero in °C (default 4).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already establish that this is a read-only, idempotent, non-destructive calculation. The description adds meaningful behavioral detail beyond annotations by disclosing the yeast estimation model, the 10 °C doubling rule, and the fact that yeast_percent overrides that model.

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 front-loaded with the purpose and then structured into parameter summary, yeast model, and percentage basis. It is somewhat lengthy but most content earns its place; the main parameter summary mildly repeats schema information.

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?

For an 11-parameter calculator with no output schema, the description covers purpose, key parameters, percentage conventions, and the yeast estimation/override behavior. It also indicates the calculation results in grams of each ingredient, so an agent has enough context to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents each parameter thoroughly. The description still adds conceptual meaning by summarizing the key inputs and clarifying that all percentages are relative to total flour weight, with total flour equal to 100%.

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 and resource: calculating all pizza dough doses using the direct method, producing grams of each flour, water, salt, and yeast with baker's percentages. It is immediately clear what the tool does and what outputs are conceptually provided.

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?

The description explains the main inputs and gives a clear conditional usage rule: if the yeast percentage is already known, use yeast_percent to override the time/temperature model. There are no sibling tools to differentiate against, and no explicit 'when not to use' guidance, but the operational context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools