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find_upstream_fix

Community fixes merged into magento/magento2 after a release line was cut, with per-version applicability from git apply checks; not Adobe Quality Patches, for those use find_patch. With only distribution and version it lists, newest first, every upstream fix whose code applies cleanly to that version and that the version does not contain yet. Add query (symptoms, error text, feature words or an id like magento2-40867), modules or categories (QPT category names like "GraphQL", "Catalog/Product") to get a ranked list with a 0-100 relevance score and the signals that matched. fixedIn filters by the first release that contains the fix (null for fixes not in any release yet). Mage-OS always returns status "unknown"; a version outside the checked release tags returns status "unknown-version".

Input Schema

NameRequiredDescriptionDefault
limitNoRanked mode only, default 10
queryNoFree text: symptoms, error message, feature names, or an upstream fix id
fixedInNoOnly fixes first released in this version, e.g. "2.4.9"; null for fixes not in any release yet
modulesNoModule names with or without the magento/module- prefix
versionYesExact Magento version, e.g. "2.4.8-p3"
categoriesNoQPT category names
distributionYes
includeExcludedNoAlso return entries excluded as features, refactors or test-only changes; each hit keeps its excluded reason

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does well: it discloses default ordering (newest first), the clean-apply/not-yet-contained filter, the ranked-mode relevance scoring with matched signals, and two special status values ('unknown' for Mage-OS, 'unknown-version' for out-of-range versions). It stops short of describing the full response shape or any rate/latency behavior, so it is strong but not exhaustive.

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?

Front-loaded with the core purpose, then progressively narrows to filters and edge-case statuses. It is dense and slightly long, but each clause carries distinct operational information (mode switch, status codes, exclusion behavior) rather than filler.

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 an 8-parameter read tool with no annotations and no output schema, the description covers the two operating modes, filter semantics, and exceptional status values that an agent must handle. The main remaining gap is the structure of returned entries in ranked vs list mode, which the description gestures at but does not fully specify.

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 already 88%, so the baseline is 3, and the description adds genuine meaning: query accepts symptoms, error text, feature words or an id formatted like 'magento2-40867'; modules accepts names with or without the magento/module- prefix; fixedIn is 'the first release that contains the fix'. These examples go beyond the terse schema descriptions.

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?

States a specific verb and resource ('community fixes merged into magento/magento2 after a release line was cut') plus the per-version applicability model. It explicitly separates itself from the closest sibling by naming find_patch and the different dataset (Adobe Quality Patches).

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

Usage Guidelines5/5

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

Gives an explicit when-not with a named alternative ('not Adobe Quality Patches, for those use find_patch'), then describes two distinct invocation modes: bare distribution+version lists everything, while adding query/modules/categories switches to ranked mode. FixedIn and includeExcluded semantics are also explained as usage conditions.

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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