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seang1121

Sports Betting MCP

analyze_game

Runs a multi-agent analysis on a game using 12 specialized agents to evaluate odds, injuries, and line movements, delivering a consensus pick with confidence and edge breakdown.

Instructions

Run full multi-agent analysis on a specific game. Uses 12 specialized agents to evaluate every angle.

Args: sport: Sport — 'nba', 'nhl', 'ncaab', or 'mlb' game_id: Game ID from the get_live_odds tool

Returns: Complete multi-agent analysis with consensus pick, confidence, and edge breakdown.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sportYes
game_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description must convey all behavioral traits. It mentions '12 specialized agents' and the return structure (consensus, confidence, edge), but omits details like execution time, failure modes, or required permissions. This is adequate but not comprehensive.

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 one-sentence purpose, followed by two bullet-like lines for parameters and return. Every word adds value, with no fluff or repetition. Front-loading the core action makes it easy to scan.

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?

Given two simple required parameters, no nested objects, and an output schema, the description provides sufficient context. It explains the input sources and output structure. The sibling tools list confirms this is the only deep analysis tool, so the description stands alone well.

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 input schema provides no descriptions (0% coverage), so the description adds significant value by clarifying the 'sport' allowed values and sourcing 'game_id' from get_live_odds. This meaningfully aids correct parameter usage beyond the raw schema.

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?

The description uses a specific verb ('analyze') and clearly identifies the resource ('game'). It distinguishes from sibling tools (get_live_odds, get_todays_picks, etc.) by highlighting the multi-agent analysis scope, leaving no ambiguity about the tool's unique function.

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?

The description explicitly states the purpose and references the source of game_id ('from the get_live_odds tool'), which guides correct invocation. However, it does not include when-not-to-use or alternative tools for similar tasks, so it falls short of a 5.

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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