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parthakker

NFL Analytics MCP

by parthakker

kalshi_markets

Retrieve Kalshi NFL market prices for game, spread, total, win totals, or Super Bowl outcomes, with optional team filtering. Prices shown as implied probabilities in dollars.

Instructions

Open Kalshi NFL markets. kind: game | spread | total | win_totals | superbowl. Optional team filter (canonical code, e.g. DET). Prices are probabilities in dollars (0.64 = 64c = 64% implied).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNogame
teamNo
Behavior3/5

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

No annotations are provided, so the description is the sole source for behavioral context. It adds valuable details such as prices being probabilities in dollars (0.64 = 64c = 64% implied) and the team filter using canonical codes. However, it does not clarify what 'open' means, whether markets are paginated, or if any authentication or rate limits apply, leaving some uncertainty.

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

Conciseness5/5

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

The description is two short sentences that front-load the primary purpose and pack in necessary semantics: kinds, team filter, and price interpretation. Every phrase earns its place, with no redundant 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 simple two-parameter listing tool with no output schema or annotations, the description covers the essential aspects: what the tool does, how to filter, and how to interpret prices. It lacks details about return structure or pagination, but given the tool's apparent simplicity, it is reasonably complete.

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?

The schema has no property descriptions (0% coverage), so the description compensates by listing valid values for 'kind' and explaining the 'team' parameter with an example (e.g., DET). It does not explicitly state defaults, but the schema already provides those, and the description effectively communicates parameter meaning.

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

Purpose4/5

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

The description clearly states the tool opens Kalshi NFL markets and enumerates market kinds (game, spread, total, win_totals, superbowl), with an optional team filter. It is specific about the resource and scope, but does not explicitly differentiate from sibling tools that handle market details or history, so it does not earn a 5.

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

Usage Guidelines2/5

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

No explicit guidance is given on when to use this tool versus siblings like kalshi_market_detail or kalshi_price_history. The description implies it is for listing open markets, but there is no mention of alternatives, exclusions, or use cases, leaving usage decisions to the agent.

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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