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parthakker

NFL Analytics MCP

by parthakker

betting_board

Compare Vegas lines against live Kalshi prices for upcoming NFL games. Identifies fee-adjusted market dislocations and situational angles to spot betting opportunities.

Instructions

Upcoming games: Vegas lines vs live Kalshi prices with dislocation flags (market-vs-market, fee-adjusted) and situational angles.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
weekNo
Behavior2/5

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

With no annotations, the description carries the burden of disclosing behavioral traits. It mentions the data content but does not disclose output structure, whether the operation is read-only, data freshness, pagination, or any potential side effects. The description is a feature list rather than a behavioral disclosure.

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 extremely concise, a single sentence (fragment) that front-loads the core concept ('Upcoming games'). It includes dense information—Vegas lines, Kalshi prices, dislocation flags, fee adjustment, situational angles—without any wasted words or redundancy.

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

Completeness2/5

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

The tool has no annotations, no output schema, and a single parameter. The description explains the board's content but omits the week parameter entirely and does not describe return format or usage caveats. It is partially complete for a simple tool but leaves significant gaps for an agent to use it correctly.

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

Parameters1/5

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

The schema has one parameter (week) with 0% description coverage, and the tool description does not mention it at all. The agent receives no explanation of what 'week' controls, its default behavior, or its allowed range. The description fails to compensate for the schema's lack of detail.

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 identifies the resource (betting board) and its key contents: upcoming games, Vegas lines vs live Kalshi prices, dislocation flags, and situational angles. It distinguishes itself from siblings like kalshi_markets and predict_game by focusing on line comparisons and dislocations. However, it lacks an explicit action verb (e.g., 'gets', 'shows'), making the purpose slightly implicit.

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 guidance is provided on when to use this tool versus alternatives like kalshi_markets or kalshi_snapshot_now. The description does not mention any scenarios, prerequisites, or exclusions. It is a purely descriptive summary without decision-making support for tool selection.

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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