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MaxWilk

poe2-build-mcp

by MaxWilk

optimize_passives

Allocate passive points to maximize a specified metric, such as TotalDPS or balanced offense/defense, using greedy search. Supports weighted goals and required nodes.

Instructions

Greedily allocate passive points to maximize a goal on the active build.

Three goal modes:

  • single metric (e.g. "TotalDPS", "Life", "TotalEHP") — maximizes that stat;

  • metric="balanced" — raises offense AND defense (relative TotalDPS + TotalEHP);

  • goals={"TotalDPS":0.5,"Life":0.3,"CritChance":0.2} — a WEIGHTED mix (relative gains, so stats on different scales combine). A goal whose base is ~0 (e.g. crit on a non-crit build) contributes nothing — fix the base first.

require=[node ids/names] allocates those nodes (+ shortest path) first, then optimizes the rest — but only as far as the budget allows (it never over-allocates; skipped requires are reported in requireSkipped). reset=True first deallocates the current tree (keeping ascendancy) so you can RE-PLAN from scratch — e.g. reset=True, require=[jewel socket ids] to rebuild the tree around jewel sockets instead of piling onto a full tree. points defaults to 0 = the FULL remaining passive budget (the usual intent — allocate the whole tree); pass a positive number only to CAP allocation. Ascendancy is a SEPARATE 8-point pool, auto-allocated on top regardless of points. Returns chosen nodes with per-step gains; pointsRemaining is the build's TRUE unspent passive points. Bounded greedy search, not a global optimum.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
metricNoTotalDPS
pointsNo
node_typeNoNotable
candidatesNo
goalsNo
requireNo
resetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description fully discloses algorithm traits: greedy search (not global optimum), separate ascendancy pool, reset deallocation behavior, and potential skipping of required nodes. It also notes return includes per-step gains and remaining points.

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 comprehensive but somewhat lengthy; it is well-structured with clear sections for goal modes and parameter explanations. A bit more conciseness would improve, but it remains efficient.

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?

Given the complexity of 7 parameters and an output schema, the description covers all key aspects: goal modes, constraints, behaviors, and return format. It provides sufficient context for an agent to use the tool correctly.

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 coverage is 0%, but the description adds rich semantics for all parameters: explains metric values, goals as weighted mix, require and reset behavior, and points as budget cap vs full allocation. It compensates fully for lack of schema documentation.

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 clearly states the tool greedily allocates passive points to maximize a goal on the active build. It distinguishes from siblings like alloc_passive and search_passives by focusing on optimization rather than manual allocation or searching.

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 when to use the tool, detailing three goal modes, the require and reset parameters, and the points default. It implicitly distinguishes from manual allocation tools but does not explicitly list when not to use it.

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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