v12-dfs

betting_sharp

Sharp

SHARP value — FanDuel vs PINNACLE (the sharpest book, ~2% hold). Pinnacle's de-vigged line ≈ TRUE probability, so this is the most reliable value signal in the kit, NO model needed: edge_vs_sharp = FD's price minus the sharp's true prob; POSITIVE means FanDuel pays MORE than fair (real value vs the market). Plus total line-shop. Pinnacle only lists UPCOMING games (gone once started).

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`	Yes

TDQS

A3.6/5.0

Behavior4/5

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

Discloses behavioral traits such as data availability (upcoming games only), the calculation methodology, and possible response codes (200, 422). With no annotations, this covers key aspects, though lacks discussion of idempotency or rate limits.

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 contains relevant information but is verbose with a full example response and unclear phrase 'Plus total line-shop.' Could be streamlined for efficiency.

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

Completeness3/5

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

Explains the core concept and response codes, but lacks a clear description of the output structure. Without an output schema, more detail on the return value fields is needed for completeness.

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?

The description does not explain the 'team' and 'date' parameters despite 0% schema coverage. No guidance on valid values or how they affect results, leaving the agent to guess.

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 identifies the tool as computing a sharp value signal by comparing FanDuel vs Pinnacle, with an explicit formula for edge calculation. It distinguishes itself from siblings like betting_scan_edges by emphasizing reliability and no-model-needed nature.

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?

Provides strong usage context: when to use (most reliable value signal, no model needed) and data freshness constraint (Pinnacle only lists upcoming games). Does not explicitly exclude alternatives or compare to siblings.

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

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