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gpaul-mcp

Ravelry MCP Server

by gpaul-mcp

Adjust a pattern to my gauge

adjust_for_gauge
Read-onlyIdempotent

Compare your swatch gauge with a pattern gauge to decide whether to change needles, predict finished size, and recalculate pattern counts so the size stays right.

Instructions

Compares the user's swatch with a pattern's gauge: whether to change needles, how big the piece comes out if they follow the pattern as written, and pattern numbers (cast-on, stitch counts, rows) recalculated for their gauge so the size stays right. Give the pattern gauge, or a pattern_id to read it from Ravelry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countsNoNumbers from the pattern to recalculate for the user.
my_gaugeYesThe user's swatch, measured after blocking if possible.
pattern_idNoReads the gauge from Ravelry.
measurementsNoFinished measurements from the pattern, to see what the user would get.
pattern_gaugeNoThe gauge printed in the pattern.
stitch_multipleNoStitch repeat, e.g. "multiple of 4 + 2", so new counts still fit it.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
notesYes
patternYes
verdictYes
needle_adviceYes
yours_per_10cmYes
adjusted_countsYes
as_written_scaleYesWidth factor if the pattern is followed unchanged: 1.1 = 10% bigger.
pattern_per_10cmYes
row_difference_percentYes
measurements_as_writtenYes
stitch_difference_percentYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.8.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is covered. The description adds real behavioral context beyond that: exactly what is computed (needle-change decision, resulting size, recalculated cast-on/stitch/row counts) and that Ravelry may be consulted for the gauge, which is consistent with the openWorldHint.

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?

Two sentences, zero filler, and the core purpose (what is compared and what is produced) is front-loaded before the input hint. Every clause earns its place.

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

Completeness4/5

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

For a 6-parameter, nested-object tool with an output schema and full annotations, the description covers purpose, input modes, and the nature of the results, so an agent can call it correctly. The only gap is explicit routing against similar sibling calculators, which keeps it short of a 5.

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 baseline is 3, but the description adds meaning the schema does not: it explains the either/or relationship between pattern_gauge and pattern_id, and frames counts/measurements as the pattern-side inputs to be recalculated. This clarifies the alternative input paths rather than merely restating field docs.

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+resource (compares the user's swatch with the pattern gauge) and enumerates the three concrete outputs: needle advice, projected size, and recalculated pattern numbers. This clearly separates it from siblings like count_stitches or yarn_needed, which do not do gauge reconciliation.

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

Usage Guidelines3/5

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

It gives input-usage guidance ('Give the pattern gauge, or a pattern_id to read it from Ravelry'), which is useful, but never states when to pick this tool over alternatives such as count_stitches, spread_evenly, or yarn_needed. Usage is implied rather than explicit, with no exclusions.

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