v12-dfs

run_mlb_postmortem

Run Mlb Postmortem

Responses:

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

Example Response:

{
  "status": "Status",
  "generated_at": "Generated At",
  "date": "Date",
  "game_id": "Game Id",
  "entries": 1,
  "field_lineups": 1,
  "training_rows": 1,
  "missing_sources": [
    "string"
  ],
  "results": {},
  "history_validation": {},
  "field": {},
  "diagnostics": {},
  "simulator_feed": {},
  "warnings": [
    "string"
  ]
}

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`	Yes	Slate date YYYY-MM-DD
`game_id`	Yes	V12 slate/game handle, e.g. 129715.
`history_csv`	Yes	Raw FanDuel contest-history CSV content.
`upload_csvs`	No
`sabersim_csv`	No	Optional external lineup export for comparison.
`sync_supabase`	No	Upsert simulator training rows to production store when configured.
`top_field_rank`	No
`history_filename`	No		contest-history.csv

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedJun 17, 2026

TDQS

D1.7/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. The response structure suggests a read operation (e.g., 'generated_at', 'results'), but there is no explicit statement of safety, side effects, or required permissions. The description fails to disclose 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.

Conciseness2/5

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

The description is short but is dominated by verbose JSON response examples that do not help an agent understand tool purpose. The actual useful content is limited to the first line. Better structure would front-load a brief functional summary.

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

Completeness1/5

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

Given 8 parameters, no output schema, and no annotations, the description is severely lacking. It does not explain expected inputs, outputs, or operational context. The tool likely involves complex postmortem analysis, but no such detail is provided.

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?

The description does not mention any parameters or add meaning beyond the input schema. With 63% schema coverage, baseline would be 3 if the description contributed, but it adds nothing. The response examples do not clarify parameter usage.

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

Purpose2/5

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

The description repeats the tool name 'Run Mlb Postmortem' but provides no definition of what a postmortem entails. It lists response fields like 'status' and 'entries' but does not explain the tool's core function or how it differs from siblings like 'betting_matchup' or 'generate_lineups'.

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

Usage Guidelines1/5

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

No guidance is given on when to use this tool vs alternatives. There is no mention of prerequisites, context, or exclusions. The description solely consists of API response examples, which are irrelevant for decision-making.

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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