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flash-props-api

Find all active props for a player

find_player_props
Read-onlyIdempotent

Every active prop for one player across today's board for a sport; same rows as scan_props, filtered by name (exact normalized match preferred, case-insensitive contains match as a fallback; see matchType in the response) instead of stat. Read-only. No side effects. Requires an API key. When to use: you know the player but not which game or event they are in, and you want their posted lines. When not to use: get_prop_evidence explains ONE prop with its line, gap and form; get_player_context gives season baselines and recent form but no posted lines; scan_props is the whole board.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPlayer name or gamertag, e.g. "Judge" or "Shotzzy"
sportNoSport id. Defaults to the in-season sport.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the annotations, the description discloses the matching fallback behavior (exact normalized match preferred, case-insensitive contains fallback), the matchType field in the response, the requirement for an API key, and the read-only/no-side-effect guarantee. This adds meaningful behavioral context beyond what annotations already provide.

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 densely packed and front-loaded with the core behavior and differentiation. A few phrases like 'Read-only. No side effects.' slightly repeat annotations, but every other sentence earns its place, especially the when-to-use/when-not-to-use section.

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 simple 2-parameter schema, high schema coverage, and no output schema, the description provides enough context for an agent to decide when to invoke this tool and what to expect. It explains the filtering behavior, the matching semantics, and the relationship to scan_props, covering the key knowledge needed to call it correctly.

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 description coverage is 100%, so the baseline is 3. The description adds extra value by explaining how the name parameter behaves (exact match preferred, contains fallback, matchType in response), which goes beyond the schema's simple 'Player name or gamertag' example. It does not add much about the optional sport parameter, but the schema already covers it.

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 states a specific verb and resource: 'Every active prop for one player across today's board for a sport.' It also explicitly differentiates itself from siblings by positioning it as the same rows as scan_props filtered by name instead of stat, so an agent can distinguish it without opening schemas.

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?

It provides explicit 'When to use' and 'When not to use' guidance, naming get_prop_evidence, get_player_context, and scan_props as alternatives with clear criteria for choosing between them. This is direct routing information with no inference required.

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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