v12-dfs

scout_field

Scout Field

VIP-only (owner order 8/16): opponent-scouting profiles for the small single-entry band. The owner's $65 table runs the SAME regulars nightly; this returns each regular's fingerprint — days seen, avg score, chalkiness (overlap with field consensus), favorite pitchers/bats and head-to-head vs the owner — built from harvested full fields (brain/scout_profiles.json, refreshed by the local scouting pipeline; packaged snapshot as fallback so the endpoint never 500s on a fresh volume). Same fail-closed allowlist as the semaforo.

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
`contest`	No		Knuckleball
`min_days`	No

TDQS

B3.1/5.0

Behavior4/5

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

With no annotations, the description carries the full transparency burden and succeeds by disclosing VIP-only access, data sourcing from harvested fields, a fallback snapshot to prevent 500s, and fail-closed allowlist behavior. It also lists what fields are returned, though it omits any rate limits or side-effect information.

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 core description is moderately concise and front-loaded with the purpose and access restriction. However, the included 'Responses' section with a 422 validation error example is boilerplate that adds little value for an agent, making the overall structure slightly cluttered.

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?

For a tool with no output schema and two undocumented parameters, the description gives a strong sense of the return content but misses essential invocation details. It does not clarify how contest and min_days affect the response, nor does it provide a complete result structure, leaving an agent uncertain about expected inputs and outputs.

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 does not explain the two parameters (contest, min_days) at all. The mention of 'days seen' is insufficient to infer that min_days filters the results, and contest is never tied to the contest selection. The description fails to compensate for the schema's lack of parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies scout_field as returning opponent-scouting fingerprint profiles for a specific single-entry band, with explicit details like days seen, avg score, chalkiness, and favorite pitchers/bats. It uses the verb 'returns' and describes a unique resource, though it does not explicitly contrast with sibling tools beyond referencing the semaforo allowlist.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage context is implied through the VIP-only restriction and the specific scenario of the owner's $65 table, but there is no explicit guidance on when to use this tool versus alternatives like contest_semaforo or generate_lineups. The fail-closed allowlist is mentioned but not framed as a decision rule.

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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