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DanielTomaro13

sportsdata-mcp

tab_match_markets

Read-onlyIdempotent

Get betting markets, selections, and prices for one match by passing sport, competition, and match name. Returns lean market data with bet options and propositions.

Instructions

Just the markets + selections + prices for one match (leaner than the full match object).

Returns: {markets:[{id, name, betOption, bettingStatus, propositions:[{name, returnWin}]}], betOptionPriority}

Example: Markets for one AFL match {"sport": "AFL Football", "competition": "AFL", "match": "Adelaide v Geelong"}

Auth: works without a key; TAB_CLIENT_ID or TAB_CLIENT_SECRET unlocks more if set.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
matchYesMatch name, e.g. "Adelaide v Geelong". Pass raw spaces. Required — part of the URL path.
sportYesSport name, e.g. "AFL Football". Required — part of the URL path.
competitionYesCompetition name, e.g. "AFL". Required — part of the URL path.
jurisdictionNoState jurisdiction.NSW
Behavior4/5

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

Annotations already flag read-only, idempotent, and open-world behavior. The description adds value by disclosing auth requirements (works without a key, unlocks more with credentials) and detailing the response shape. This is useful beyond the annotations, though it doesn't cover potential edge cases like rate limits.

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 description is compact and well-structured: a one-line summary, return type, example, and auth note. It avoids redundancy and is front-loaded with the primary purpose. Slightly verbose with the example but necessary for clarity.

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?

The tool is simple with four parameters and no output schema. The description provides the return structure, an example, and auth context. While it doesn't mention any limits or pagination, it's likely unnecessary for a lean market query. The description is sufficiently complete for its complexity.

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?

The input schema provides 100% coverage with descriptions for all parameters, including the requirement to pass raw spaces. The description adds an example but does not enhance parameter understanding beyond what the schema already offers. Per rubric, with high coverage, a baseline of 3 is appropriate.

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 clearly states the tool returns "the markets + selections + prices for one match" and highlights it is leaner than the full match object. It includes an explicit return structure and an example, making the purpose unambiguous and distinguishing it from the likely sibling tab_match.

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 phrase "leaner than the full match object" signals when to prefer this tool over a more detailed alternative. It does not explicitly name tab_match as the alternative, but the implication is clear. The example also demonstrates expected usage, providing adequate context for 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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