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v12-dfs

by io.github.Jorgeperez0825

generate_lineups

Generate Lineups

Responses:

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

Example Response:

{
  "status": "Status",
  "generated_at": "Generated At",
  "warnings": [],
  "exposures": {},
  "lineups": []
}

422: Validation Error Content-Type: application/json

Example Response:

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYesSlate date YYYY-MM-DD
runsNoAccepted for tier limits and response metadata. Portfolio quality is controlled by preset, candidate_pool_size, pool_oversample_multiplier, and simulation settings.
seedNoRandom seed for reproducibility
locksNoPlayers to lock in
sportNoSport engine to run: nba or mlb.nba
styleNo
avoidsNoPlayers to avoid
n_simsNoMonte Carlo iterations when simulation is enabled.
presetNoStrategy preset key.gpp_pro_pattern
lineupsNoNumber of lineups (overrides preset default)
platformNoV12 platform profile: fd or dk.fd
sim_modeNoSimulation mode. NBA: mvn/off. MLB: on/off.
slate_idNoSpecific slate ID from /v1/slates. Overrides date-based lookup.
strategyNoGPP or cash game strategy
plan_onlyNoWhen true, the server resolves preset / contest / authoritative slate / top-run-env / locks but does NOT run the optimizer. Returns a Plan response so the user can review and confirm before spending credits on a real generate. Costs 0 credits; normal slate-open safety checks still apply.
contest_idNoOptional contest handle for building an entry-update CSV from the user's existing contest entries.
fd_user_idNoDEPRECATED on /v1/generate: fd_user_id is bound server-side to your API key (see /v1/admin/bind-fd-user). Body value is IGNORED on the public route unless X-Internal-Secret matches. Kept on the schema for the internal admin path only.
focus_gamesNoGame keys to emphasize, e.g. ['PHI@BOS']. Applies a small documented projection nudge.
allow_lockedNoExplicit replay/late-swap override. Default false blocks slate_id generation once any game has started.
refresh_dataNoForce a fresh V12 data refresh for user-facing generation. Backtests should set false.
exposure_capsNoPer-player exposure caps {name: 0.0-1.0}
contest_fallbackNoEscape hatch when a contest_id cannot be mapped to a slate (contest_slate_unresolved). 'slate_only' + an explicit slate_id builds on THAT slate and skips contest-specific behavior (field-size routing, entry-template mapping) instead of failing the whole generate. Requires slate_id; without it the request still fails closed. Omit for normal contest-anchored builds.
contest_field_sizeNoTotal entries (field size) of the selected contest. Governs the MLB objective: field <= 10 -> max-mean (mlb_small_field_se); field > 10 -> ceiling (mlb_se_ceiling) with a continuous ceiling tilt and the real field size fed into contest simulation. A matched contest_id lobby row is authoritative; this value is the fallback when that row is unavailable.
entry_template_csvNoRaw CSV of a FanDuel entries-upload-template the operator already downloaded. When present, the entry-upload is filled from its OWN entry_ids locally — no FanDuel API / session / PerimeterX call. Fixes fd_entry_template_auth_expired when the user already has the template in hand.
team_exposure_capsNoPer-team portfolio caps, as [["COL",0.5],...] pairs or {"COL":0.5} dict.
candidate_pool_sizeNoExact candidate pool size for Pool→Rank→Keep-N selection. 0/None uses preset multiplier.
contest_max_entriesNoMax entries per user for the selected contest. Used to identify multi-entry mass GPPs (>=4) which keep the caller's preset. Field SIZE, not this, governs the MLB objective — see contest_field_size.
max_hitter_exposureNoPortfolio cap: max share of delivered lineups any ONE hitter may appear in (0.4 = 40%). Pitchers exempt.
max_pitcher_exposureNoPortfolio cap: max share of delivered lineups any ONE pitcher may appear in.
projection_overridesNoUser-uploaded projection/ownership rows. Supports name/player_id/team/position/projection/ownership/ceiling/floor/stddev.
auto_team_exposure_capNoDefault per-team portfolio cap (share of lineups containing ANY given team). Per-team entries in team_exposure_caps win over it.
pool_oversample_multiplierNoCandidate pool multiplier when candidate_pool_size is not set.

TDQS

B3.4/5.0
Behavior4/5

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

Even though no annotations are provided, the description reveals valuable behavioral context through parameter documentation: plan_only does NOT run the optimizer and costs 0 credits, allow_locked controls the replay/late-swap override, refresh_data notes backtests should set false, and contest_fallback explains failure modes and fail-closed behavior. This gives the agent meaningful operational awareness beyond the name and schema. However, the core description does not explicitly state that generate_lineups can be expensive or that it may make external API calls.

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

Conciseness2/5

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

The description is dominated by a large response-example block that repeats schema-like fields ('Status', 'Generated At', 'warnings', etc.) without explaining actual response semantics. The useful behavior notes are buried inside parameter descriptions rather than in a concise overview. The core summary is only the two-word phrase 'Generate Lineups', which is under-specified and not front-loaded with operational guidance.

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?

For a complex 32-parameter tool with no annotations and no output schema, the description is incomplete: it does not explain the returned lineup structure, credit costs, required data prerequisites (e.g., needing a valid slate), or error handling beyond a generic 422 example. The parameter descriptions and plan_only flow help, but the description itself lacks a coherent narrative tying the operation together. The rich schema compensates somewhat, so it is not a 2, but it is far from complete.

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

Parameters3/5

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

Schema description coverage is 97%, so the schema already documents almost every parameter thoroughly. The description adds some value through its plan_only, contest_fallback, and refresh_data notes, but most of the parameter meaning is already present in the input schema. The response examples are not parameter documentation, and some descriptions are copied from schema rather than enriched.

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 title 'Generate Lineups' is clear about the verb and resource, and the description context plus parameter hints (slate date, sport, strategy, exposures) make it evident this generates DFS lineups. However, it does not explicitly distinguish itself from sibling tools like get_slate_players or fill_template, and the description itself is mostly response examples rather than a clear statement of what the tool does.

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?

The description does not explicitly state when to use this tool vs alternatives, but the parameter hints (e.g., slate_id, contest_id, plan_only) imply it is the main lineup generation endpoint. It lacks guidance on when to prefer other tools such as list_slates, scout_field, or plan_only for review. The plan_only parameter does provide a clear 'use this to preview before spending credits' cue, which is helpful.

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