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Everloot: WoW Forever data

Recipe skill ranges

recipe_thresholds
Read-only

Where a Forever profession's recipes turn yellow, green and grey, inferred from the colours players' trade-skill windows showed. A recipe appears only after 3 installs agree, as ranges, never guessed exact numbers. Agreed reagents come with an estimated cost per craft when every reagent has an agreed price. Reads Everloot's current public corpus.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
recipeNoOptional part of a recipe name to narrow the list.
professionYesProfession name, such as Alchemy.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
reasonNoWhy nothing was read, when connected is false.
dataAsOfYesThe corpus snapshot time, or null when unrecorded.
connectedYesFalse when no corpus was read; the page is then absent, which is not a measured zero.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Beyond the readOnly/openWorld/destructive annotations, the description discloses meaningful behavioural traits: data is inferred from players' trade-skill windows, a recipe is only surfaced when 3 installs agree, results are ranges never exact numbers, and cost per craft appears only when every reagent has an agreed price. This provenance and confidence detail is genuinely useful, though nothing is said about coverage, staleness limits, or error behaviour.

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?

Four dense sentences with the core purpose front-loaded and no filler. The colour-metaphor phrasing is slightly indirect but each sentence contributes distinct information about provenance, thresholds, and cost estimates.

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?

With an output schema present, return values need not be explained, and the description covers provenance, confidence rules, and cost caveats. It is nearly complete for this tool, missing only guidance on corpus freshness or how to interpret the returned ranges.

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 coverage is 100%, so both parameters are already documented in the schema, and the description adds no extra syntax, matching rules, or format detail for the 'recipe' substring filter. The baseline of 3 applies when the schema carries the parameter burden.

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 states a specific resource (a profession's recipe skill thresholds where recipes turn yellow/green/grey) and implies retrieval, so an agent understands what it returns. It does not name or contrast with siblings such as profession_path or talent_plan, leaving differentiation to the tool name alone.

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?

Usage is only implied: an agent can infer this answers 'when do recipes change colour for profession X'. There is no explicit when-to-use, no exclusions, and no named alternative for related questions, so the routing burden falls on the reader.

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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