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kashrockapi

kashrock-mcp

Official
by kashrockapi

get_player_props

Retrieve DFS and sportsbook player props for esports matches, filterable by player, market, book, sport, and event. Returns slim grouped data to avoid oversized payloads.

Instructions

DFS + sportsbook named-player props. Default view=groups + slim — not the full 8MB dump.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bookNo
slimNo
viewNogroups
boardNoall
limitNo
sportYes
marketNo
offsetNo
playerNo
event_idNo
player_idNo
market_containsNo
include_event_linesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.5

TDQS

C2.9/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It usefully discloses that the default view is groups+slim and warns against the full 8MB dump, which is meaningful behavioral context. It does not mention pagination behavior, return format, or any access or side-effect concerns.

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 a single compact sentence with the most important payload warning front-loaded. Every word earns its place, though the brevity contributes to the incompleteness on parameters and usage.

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

Completeness2/5

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

For a tool with 13 parameters, no annotations, and no output schema, this description is far from complete. It addresses the large-payload concern but leaves output structure, filtering semantics, pagination, and use-case routing unexplained.

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 0%, so the description needed to compensate across 13 parameters, but it only adds context around the 'view' and 'slim' defaults. The remaining parameters (book, market, player, event_id, market_contains, include_event_lines, etc.) are left entirely to inference from their names.

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 identifies a specific resource ('DFS + sportsbook named-player props') and differentiates the tool from generic prop tools by the 'named-player' qualifier. However, it lacks an explicit verb and doesn't directly name sibling tools, so it stops short of being immediately distinguishable from get_props or get_player_board.

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?

No guidance is given about when to use this tool versus alternatives like get_props or get_player_board. The mention of the default slim/grouped view implies a preference for smaller payloads, but it does not state selection criteria or exclusions.

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