v12-dfs

fill_template

Fill Template

Fill a downloaded FanDuel/DK entries-template with already-generated lineups WITHOUT re-running the optimizer — the MCP-callable equivalent of the dashboard's fill_template. Keeps each entry row's leading columns (entry_id, contest_id, fee) and replaces the roster-slot columns with a lineup's upload tokens, cycling lineups if there are more entry rows than lineups.

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
`lineups`	Yes	Each lineup = upload tokens in template roster-slot order.
`template_csv`	Yes	Raw CSV of the entries-template (headers + entry rows).

TDQS

A4.5/5.0

Behavior5/5

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

With no annotations provided, the description carries full burden and delivers: it discloses the tool is non-destructive (fills template without re-running optimizer), details the specific column manipulation, explains lineup cycling behavior for excess entry rows, and includes response codes (200, 422) with an example. This fully informs the agent of behavioral traits.

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

Conciseness4/5

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

The description is well-structured with a clear title, a concise purpose statement, and a bullet-like list of behavioral details. It includes a response example, which adds some length but is helpful. Overall, it is front-loaded and efficient, though the example could be slightly trimmed.

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 no annotations or output schema, the description adequately covers the tool's behavior, parameters, and potential responses. It explains the input format (CSV with headers), the transformation logic, and error handling (422). Minor gaps exist regarding malformed input or specific validation, but overall it is complete for a tool with two well-documented parameters.

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

Parameters4/5

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

Schema description coverage is 100%, but the description adds value by explaining the process context: 'keeps each entry row's leading columns... and replaces the roster-slot columns with a lineup's upload tokens'. This clarifies the relationship between the parameters and the expected transformation, going beyond the schema descriptions alone.

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 specifies the tool's purpose: 'Fill a downloaded FanDuel/DK entries-template with already-generated lineups WITHOUT re-running the optimizer.' It clearly distinguishes from the sibling tool 'generate_lineups' by explicitly stating it avoids re-running the optimizer, and it positions itself as the MCP-callable equivalent of the dashboard's fill_template.

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?

The description explains when to use the tool: after lineups have been generated, to fill a template without re-optimizing. It details what the tool does (keep leading columns, replace roster-slot columns, cycle lineups). While it does not explicitly name alternatives or state when not to use, the context of the sibling tools and the instruction to use after generation provides adequate guidance.

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.

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