v12-dfs

contest_advice

Contest Advice

Advice for a concrete contest name. Resolves the name against the LIVE lobby FIRST so a real live contest is never reported missing (the curated registry is only a subset — that was the "$800 Wed MLB Home Run no existe" bug), then layers registry strategy advice on top when a template matches.

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	Default
`name`	Yes
`sport`	No	MLB
`platform`	No	fd

TDQS

A3.9/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It explains the resolution order (live lobby first, then registry) and mentions response codes (200, 422). The tool is advisory and read-only, so side effects are not a concern. The behavioral description is detailed enough for an agent to understand the process.

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 moderately sized with a clear title, a paragraph explaining the process, and a response section. It is well-structured and front-loaded with the purpose. The bug fix story adds valuable context but is slightly verbose.

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?

The description adequately explains the core functionality (advice for contest names) and the resolution logic. However, it lacks explanation of the sport and platform parameters, and the return format is only partially illustrated (example response is a validation error). Without output schema, more detail on the advice content would be beneficial.

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

Parameters2/5

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

Schema description coverage is 0%. The description only implies the 'name' parameter is a contest name, but does not explain 'sport' or 'platform' (though they have defaults). This is insufficient for an agent to use these parameters correctly without guessing.

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 states the tool provides 'Advice for a concrete contest name' with a specific two-step process (resolve against live lobby then layer registry advice). This distinguishes it from sibling tools like list_contests or list_live_contests which only list contests without providing strategic advice.

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 gives clear context on when to use the tool: when you have a contest name and want advice, including a note about the bug fix. However, it does not explicitly state when not to use it or provide alternatives, though the sibling list context is available.

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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