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by predictionmarketspicks
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JavaScript
Remote

NFL Edge (model vs Kalshi)

nfl_edge
Read-only

Find NFL edges on Kalshi by comparing model probabilities with live prices for weekly games, win totals, MVP, and championship odds; scan tickers for edge and side.

Instructions

Use for "which NFL games are mispriced on Kalshi" and "NFL win total, MVP or Super Bowl edges". Our NFL model vs live Kalshi prices — this week's game positions, season win totals, MVP and championship odds: model probability, price, edge and side. tickers[] checks a watchlist (futures, mvp, championship). Pro — sign in with PredictionMarketsPicks, or a Pro API key.

Input Schema

NameRequiredDescriptionDefault
limitNoMax rows (default 10). Values above 25 are clamped to 25.
marketNogame = this week moneyline edges; futures = season win totals; mvp = KXNFLMVP; championship = playoff/conference/Super Bowl. One of: game · futures · mvp · championship.
minEdgeNoMinimum absolute edge in pp (default 4) — futures / mvp / championship only; game rows are the published positions. Ignored when tickers[] is passed. Accepts a number or a numeric string ("3", "3pp", "3%").
tickersNoOptional Kalshi ticker watchlist (up to 25) — e.g. paste the tickers from your Kalshi Pro screener or Canvas to get PMP's edge on exactly those markets. Full market or 3-segment event tickers both work. Tickers PMP doesn't model are returned as not_covered (never a fabricated edge).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed9 schema fields changed
    • removedInput schema / properties / limit / anyOf
      Removed value: -[
-  {
-    "anyOf": [
-      {
-        "type": "number"
-      },
-      {
-        "type": "string"
-      }
-    ]
-  },
-  {
-    "type": "null"
-  }
-]
    • addedInput schema / properties / limit / type
      Added value: +[
+  "number",
+  "string",
+  "null"
+]
    • addedInput schema / properties / market / anyOf
      Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "enum": [
+      "game",
+      "futures",
+      "mvp",
+      "championship"
+    ],
+    "type": "string"
+  }
+]
    • removedInput schema / properties / market / default
      Removed value: -"game"
    • changedInput schema / properties / market / description
      Previous value: -"game = this week moneyline edges; futures = season win totals; mvp = KXNFLMVP; championship = playoff/conference/Super Bowl."New value: +"game = this week moneyline edges; futures = season win totals; mvp = KXNFLMVP; championship = playoff/conference/Super Bowl. One of: game · futures · mvp · championship."
    • removedInput schema / properties / market / enum
      Removed value: -[
-  "game",
-  "futures",
-  "mvp",
-  "championship"
-]
    • removedInput schema / properties / market / type
      Removed value: -"string"
    • removedInput schema / properties / minEdge / anyOf
      Removed value: -[
-  {
-    "type": "number"
-  },
-  {
-    "type": "string"
-  }
-]
    • addedInput schema / properties / minEdge / type
      Added value: +[
+  "number",
+  "string"
+]
  2. Addedv1.12.0

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint and destructiveHint=false, so safety is covered. The description adds genuinely non-structured context: a Pro auth requirement (sign in with PredictionMarketsPicks or a Pro API key) and the sourcing guarantee that unmodeled tickers return not_covered rather than fabricated edges.

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?

Front-loads the user-intent queries, then packs market scope, output fields, watchlist behavior and auth into a compact block. It is dense but not padded; the em-dash clause is slightly run-on but each phrase carries information.

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?

For a read-only tool with no output schema, the description discloses the returned fields (probability, price, edge, side), the auth gate and the tickers/not_covered behavior, which is enough to call it correctly. Minor gaps remain around pagination and the exact shape of game rows versus futures rows.

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 100%, so the schema already documents limit, market, minEdge (including its ignore-when-tickers rule) and tickers. The description only lightly reinforces the tickers watchlist use case without adding syntax or format detail beyond the schema, so the baseline 3 applies.

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?

States a specific verb+resource: an NFL model vs live Kalshi prices surfacing mispricings, with the concrete market coverage (game positions, win totals, MVP, championship) and output fields (model probability, price, edge, side). It is clearly distinguishable from siblings like nfl_prop_edge, nfl_power_ratings and nhl_edge without opening any schema.

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?

Opens with explicit when-to-use phrasing ("which NFL games are mispriced on Kalshi", "NFL win total, MVP or Super Bowl edges"), giving a clear invocation context. It does not name the alternatives an agent should prefer (nfl_prop_edge for props, scan_mispricings or nhl_edge), so there is context but no exclusions.

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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