v12-dfs

betting_game_lines

Game Lines

GAME markets — moneyline / run total / run line for today's MLB games. This answers the 'quién gana / cuántas carreras / pronóstico Yankees vs Toronto' questions that player props CAN'T (V12 used to just refuse them). Optional team filter ('Yankees', 'NYY'). MARKET data from ESPN's public scoreboard, surfaced as-is — V12 has NO game-outcome model, so this is the book's own number, never a fabricated prediction. A book may not have posted a game yet (fields null) — say so, don't invent a line.

Responses:

200: Successful Response (Success Response) Content-Type: application/json 422: Validation Error Content-Type: application/json

Example Response:

{
  "detail": [
    {
      "loc": [],
      "msg": "Message",
      "type": "Error Type",
      "ctx": {}
    }
  ]
}

Input Schema

TableJSON Schema

Name	Required	Description	Default
`date`	No
`team`	No

TDQS

A4.1/5.0

Behavior5/5

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

With no annotations provided, the description discloses that data comes from ESPN's public scoreboard as-is, that V12 has no game-outcome model, and that lines are the book's own number (never fabricated). It also warns about null fields and advises not to invent lines.

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

Conciseness3/5

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

The description is somewhat lengthy and includes a verbose response section with an example 422 response that adds little value. The main functional description is clear but could be more streamlined.

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?

Given that there is no output schema and no annotations, the description covers purpose, data source, behavior with nulls, and one parameter. However, it omits explanation of the date parameter and does not describe the response structure beyond HTTP status codes.

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?

The schema has two parameters (date, team) with 0% description coverage. The description only explains the team filter with examples like 'Yankees' and 'NYY', but does not explain the date parameter at all, leaving a significant gap.

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 it provides MLB game lines (moneyline, run total, run line) and answers specific Spanish queries like 'quién gana' and 'cuántas carreras'. It distinguishes from sibling tools by noting that player props cannot answer these questions and that V12 used to refuse them.

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?

It explicitly says when to use this tool (for game-level markets, not player props) and provides guidance on handling null fields when a book hasn't posted a game. However, it does not directly address alternatives among sibling tools like betting_market or betting_matchup.

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

All v12-dfs tools Inspect

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

C2.8/5.0

Disambiguation3/5

The betting_* cluster is clearly namespaced, but betting_best_bets and betting_scan_edges both return a ranked board of top prop edges, and betting_market overlaps with betting_game_lines and betting_sharp/cross_book in purpose. The detailed descriptions reduce misselection, but several boundaries are not crisp.

Naming Consistency3/5

The set is uniformly snake_case with helpful cluster prefixes like betting_, list_, and slate_, so it reads predictably. However, it mixes verb_noun names (generate_lineups, run_mlb_postmortem), noun phrases (betting_market, health_check/health_v12), and adjective-noun names (betting_best_bets, betting_sharp), so there isn't one consistent pattern.

Tool Count2/5

34 tools is above the 25+ threshold for a single MCP server, even considering the combined betting/DFS/contest scope. Several tools could be consolidated — betting_best_bets vs betting_scan_edges, health_check vs health_v12, and the two guides — making the surface feel heavy rather than lean.

Completeness5/5

The betting lifecycle is covered end-to-end: raw markets, models, edge scans, value checks, parlay building, bet logging/settlement, and P&L/CLV. The DFS side covers slates, player pools, lineup generation/fill, presets, contests, diff/health/refresh, and postmortems, leaving no obvious dead end for agents.

Resources

Need Help?