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League of Legends DFS projections

invoke_lol_projections
Read-onlyIdempotent

BankrollSZN League of Legends DraftKings projections for the latest slate: salary, points per series, floor, ceiling, value, kills, deaths, assists, CS, maps, win probability and team implied kills by role.

Input Schema

NameRequiredDescriptionDefault
teamNo
limitNoMaximum rows to return. The plan limit applies on top.
leagueNo
playerNo
sort_byNo
positionNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true and destructiveHint=false, so the safety profile is fully covered. The description adds the returned metric set, which is useful return-shape context, but says nothing about plan/credit limits, pagination, slate freshness or auth - all relevant for a paid data endpoint.

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?

A single front-loaded sentence naming the product, sport, platform and slate, followed by the field list. No filler, though the trailing stat enumeration reads like a return-value list crammed into a description that could have been a clause shorter.

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

Completeness3/5

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

With no output schema, enumerating returned fields (salary, floor, ceiling, value, kills/deaths/assists, CS, maps, win probability) is genuinely useful and fills the biggest gap. However, six parameters at 17% coverage and zero guidance on filtering or alternates leave the definition only partially complete for calling the tool correctly.

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

Parameters2/5

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

Schema description coverage is only 17%: just one of six parameters (limit) is documented, and even that defers to an unexplained 'plan limit'. The description hints at role-based filtering ('by role', which maps loosely to the position enum) but never explains team, league, player or sort_by semantics, so it does not compensate for the coverage gap.

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?

States a specific verb+resource: DraftKings League of Legends projections for the latest slate, with the stat set enumerated. It is clearly distinguishable in the abstract from siblings like invoke_lol_optimizer or invoke_lol_player_pool, though it never names those siblings to contrast scope.

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

Usage Guidelines2/5

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

The phrase 'for the latest slate' implies a DFS/lineup-building context but there is no explicit when-to-use, no prerequisites, and no routing to alternatives such as invoke_lol_player_pool, invoke_lol_elo or invoke_lol_optimizer. The agent gets no help choosing between the several LoL siblings.

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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