Skip to main content
Glama

flash-props-api

by io.github.iFan6oy

Rankings: top gaps, recent form, sample strength (tiered)

get_leaders

Ranked boards for a sport with a registered Flash pack. Read-only. No side effects. Tier-shaped exactly like REST: Free sees the top 3 teaser, Starter the top 10, and Pro the full board. metric=gap ranks Flash-vs-book gaps; metric=form ranks recent mean vs Flash Line; metric=sample ranks baseline size. Paywall metadata includes totalAvailable and the next unlock when rows are capped. No picks. Use list_sports to discover which sports have deep context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
statNoRestrict the gap board to one market.
limitNoMax rows before tier shaping (default 20).
sportNoSport id with a registered Flash pack. Defaults to cod.
metricNogap (default), form, or sample.

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and does so well: it explicitly states 'Read-only. No side effects,' explains tier-based row shaping, and discloses paywall metadata behavior. The metric definitions and 'No picks' line further clarify what the tool does and does not return.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence earns its place: core purpose, safety profile, tiering, metric semantics, paywall metadata, exclusions, and a pointer to a sibling tool. It is front-loaded with the most important identifying information and has no 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 a tool with no output schema and no annotations, the description covers the key behavioral context: what the board ranks, how tiering affects results, and what paywall metadata is included. It could more fully describe the response row shape or how stat interacts with non-gap metrics, but the existing coverage is strong.

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 100%, so the baseline is 3. The description adds meaningful semantics for the metric parameter by explaining what each enum value ranks, which goes beyond the schema's terse 'gap, form, or sample' description. Other parameters like stat, limit, and sport are less enriched, but the schema already documents them adequately.

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 opens with 'Ranked boards for a sport with a registered Flash pack,' giving a specific resource and operation. It further differentiates from siblings by explicitly stating 'No picks' and pointing to list_sports for discovery, so an agent can distinguish it from other read-only tools.

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?

The description gives clear context for when this tool is appropriate: when ranked boards for a Flash pack sport are needed. It explicitly names list_sports as the alternative for discovering eligible sports, and the 'No picks' statement serves as an exclusion for users seeking pick recommendations.

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

All flash-props-api toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct resource or access pattern: game lookup, game props, player props, cross-game scans, metadata, context, evidence, history, leaders, and movers. Related tools like list_games/find_game and scan_props/find_player_props are clearly separated by scope and reinforced with when-to-use guidance. No two tools appear to do the same job.

Naming Consistency5/5

All 12 tool names follow a consistent snake_case verb_noun pattern with a small set of meaningful verbs: find_* for lookups, list_* for catalogs, get_* for specific resources, and scan_* for cross-game views. There is no casing or verb-style mixing.

Tool Count5/5

12 tools is well within the ideal range and appropriate for the API's breadth: sports discovery, game discovery, multiple prop-access paths, market vocabulary, player context, evidence/history, and movement/leader boards. Each tool adds a distinct access pattern rather than redundant duplication.

Completeness5/5

The surface covers the full read-only workflow: discover sports, resolve games/eventIds, fetch props by game/player/cross-game, interpret market keys, and drill into context/evidence/history/movers/leaders. Tool outputs feed naturally into each other, so there are no obvious dead ends or missing operations for the stated domain.

Resources