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v12-dfs

by io.github.Jorgeperez0825

betting_game_model

Game Model

MODEL the GAME run markets — TEAM TOTALS (over/under) and NRFI/YRFI — vs the FanDuel line, MARKET-ANCHORED: expected_runs = the book's de-vigged implied mean + 0.25×(structural model − book), and every edge is computed vs the FAIR (de-vigged) probability, never the raw vig price. The structural inputs (season runs/game, opposing STARTER + BULLPEN run factors, park; negative-binomial tails) supply only the tilt. Entries with anchored=false / model_only=true carry edge=null — present them as context, NEVER as value. A SCREEN vs the market, not a lock — validate with CLV. For raw lines on every market use betting_market.

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
NameRequiredDescriptionDefault
dateNo
teamYes

TDQS

A3.5/5.0
Behavior4/5

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

There are no annotations, so the description carries the burden, and it is unusually detailed: it discloses the anchoring formula, de-vigged fair-probability semantics, the role of structural inputs, and the anchored=false / edge=null behavior. It only falls short of a 5 by not explicitly describing read-only behavior, response shape, or any side effects.

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?

Most of the content is dense and useful, but the description leads with a formula and heavy methodology before basics, and the Responses section is only generic error content with no success example. It is organized enough to navigate, but not front-loaded around the call itself.

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?

There is no output schema and no meaningful success response example, and the description makes no attempt to define the return fields beyond edge=null and expected_runs. For a domain-specific, formula-heavy tool, an agent still can't fully predict what a valid call returns and how to consume it.

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

Parameters1/5

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

Schema description coverage is 0%, and the description adds nothing about the actual arguments — team or date. No format, date scope, identifier type, or example is given; the 'structural inputs' it mentions are internal model inputs, not the caller-provided parameters.

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 names a specific verb and resource: 'MODEL the GAME run markets — TEAM TOTALS (over/under) and NRFI/YRFI — vs the FanDuel line'. It also explicitly separates itself from betting_market via 'For raw lines on every market use betting_market', so an agent can reliably distinguish it from close siblings.

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?

There is an explicit routing instruction to betting_market for raw lines, and the 'screen vs market, not a lock — validate with CLV' caveats help an agent understand how the result should be treated. However, it does not fully explain when to choose this over other model-adjacent siblings such as betting_game_lines, betting_analyze_prop, or betting_matchup.

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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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