Skip to main content
Glama

Prediction Markets Quant

by com.predictionmarketspicks
OverviewDiscussionsAdmin

Explain This Prop — why a player prop is a call

explain_prop
Read-only

Why PredictionMarketsPicks publishes a call on one NFL player prop this week: the Kalshi contract, the YES/NO call and Kalshi's price for that side, our tier and — where our probability is measured calibrated — our odds, our projection against the threshold, whether it is published or held at WATCH (and why), The Ladder's read on that stat, and what the model does not account for. One contract per call. Use for "why do you like 's over", "explain the prop", "why is on your board". For the full strike ladder use explain_player.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lineNoOptional contract threshold or line (e.g. 60 or 59.5) to pick one strike.
statNoOptional stat, e.g. "rec_yds", "receptions", "rush_yds", "pass_tds" or "receiving yards". Defaults to the player's strongest published call.
playerNoPlayer name, full or partial (e.g. "Josh Allen").

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • removedInput schema / properties / line / anyOf
      Removed value: -[
-  {
-    "type": "number"
-  },
-  {
-    "type": "string"
-  }
-]
    • addedInput schema / properties / line / type
      Added value: +[
+  "number",
+  "string"
+]
  2. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=false, destructiveHint=false, so the safety profile is covered. The description adds genuine behavioral context beyond that: it discloses the returned content (tier, calibrated odds, projection vs threshold, and WATCH-vs-published status including the reason) and explicitly notes what the model does not account for. It does not discuss latency, caching, or error behavior, so it stops short of a 5.

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 long enumerative sentence is dense but each item earns its place because no output schema exists to convey the response shape. Purpose and usage examples are present and the alternative-tool routing is at the end rather than the front, a minor structural weakness.

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?

With no output schema, the description correctly compensates by enumerating the returned fields and the WATCH/held distinction. Combined with the sibling pointer to explain_player, an agent has enough to call and interpret the tool; only the player-name matching semantics (full vs partial) are left entirely to the schema.

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% and all three parameters carry their own examples and defaults, so the schema does the heavy lifting. The description only implies the line/stat selection via 'One contract per call' and adds no format or default guidance beyond the schema, which is the expected baseline.

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: explaining why a published call exists on one NFL player prop, and enumerates exactly what the explanation contains (Kalshi contract, YES/NO side, price, tier, odds, projection, WATCH status). It explicitly distinguishes itself from the sibling explain_player ('For the full strike ladder use explain_player').

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

Usage Guidelines5/5

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

Provides concrete trigger utterances ('why do you like <player>'s over', 'explain the <player> prop') and names the alternative tool plus the condition (full strike ladder) that routes to it. Also scopes to 'one contract per call', which prevents misuse for multi-strike lookups.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Prediction Markets Quant toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources