Skip to main content
Glama
flattery89

Brewfather MCP Server

by flattery89

analyze_efficiency

Read-onlyIdempotent

Calculate brewhouse efficiency for recent batches using measured OG, volume, and grain bill potential. Get per-batch breakdowns and aggregate statistics (mean, median, SD) to spot trends.

Instructions

Compute brewhouse efficiency across recent batches.

For each batch: potential points from the grain bill, measured OG points, and the measured volume, combined as (og_points * volume_gal) / potential_points.

Per-fermentable potential comes from Brewfather's stored potential field when present (the figure the app itself calculates with), falling back to 46 * yield/100.

Args: limit: How many recent completed/archived batches to consider. Default 15. grain_only: Skip batches containing extract, sugar or other non-mashed fermentables, which inflate the figure. Default True. volume_basis: "fermenter" (default, matches Brewfather's own Brew House Efficiency — volume into the fermenter, so kettle losses count against you) or "post_boil" (kettle volume at flameout, which reads higher by exactly the transfer loss).

Returns: Per batch: name, brew date, target vs measured OG, target vs measured volume, computed efficiency, and Brewfather's stored figure with the delta between them. Then aggregates: mean, median, standard deviation, overall and split by batch size.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
grain_onlyNo
volume_basisNofermenter

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds valuable behavioral detail: the exact formula, fallback logic for missing potential, the impact of volume_basis on reading, and the grain_only filter. This disclosure goes well beyond what annotations provide.

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 well-structured: summary, formula, parameter explanations, and return values. Every section is purposeful and concise, with no redundancy or filler. The length is justified by the need to explain calculation nuances.

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?

Even with an output schema present, the description clearly lists the computed fields and aggregates, and explains all parameters and edge cases. It provides a complete picture for an AI agent to invoke the tool correctly and interpret results, making it fully contextual.

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

Parameters5/5

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

Schema description coverage is 0%, but the description explains every parameter in depth: limit, grain_only, and volume_basis with defaults and detailed implications. This fully compensates for the absent schema descriptions and adds meaning that the schema alone would not convey.

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 starts with 'Compute brewhouse efficiency across recent batches' – a specific verb (compute) and resource (brewhouse efficiency) with clear scope. It distinguishes from sibling tools by focusing on aggregated efficiency analysis rather than listing or retrieving individual batches.

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?

It provides clear context on when to use (analyzing efficiency across batches) and explains parameter semantics like volume_basis and grain_only. However, it does not explicitly name alternatives or state when not to use it, so it's a clear context but missing exclusions.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/flattery89/brewfather-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server