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borakilicoglu

iddaa-mcp

get_league_fixture

Retrieve archived league fixtures for major European and international competitions. Filter by league and week, and optionally apply draw-betting strategies like Martingale or Fibonacci to analyze betting scenarios.

Instructions

Fetch fixture data for selected leagues from archive source

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
weekNoWeek number (if omitted/null: all weeks)
leagueYesLeague name
localeNoLanguage for response text (default: tr)tr
baseBetNoBase bet amount
comebackNoIf true, only matches with halftime-leader reversal (1->2, 2->1) are returned
strategyNoOptional draw-betting strategy simulation
Behavior1/5

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It fails to mention the presence of simulation parameters (strategy, comeback, baseBet) that suggest non-trivial processing, nor does it confirm read-only behavior or describe any side effects. The description is a bare fetch statement with no transparency about actual tool behavior.

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

Conciseness3/5

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

The description is a single concise sentence with no redundancy. However, given the tool's complexity (six parameters including simulation logic), a one-sentence description is under-sized and leaves out critical information. It is structurally clean but not adequately informative for the breadth of the tool.

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 lacks an output schema, so the description should explain return values, but it does not. It also omits any mention of the simulation/filtering parameters (comeback, strategy) and how they affect the result. The description is too sparse for a tool with this complexity and no supplementary annotations.

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%, so all parameters have descriptions in the schema. The tool description adds no additional parameter semantics beyond what the schema already provides. The baseline score of 3 is appropriate since the schema does the heavy lifting.

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 action (fetch), the resource (fixture data), and the scope (selected leagues from archive source). It is specific enough to convey the tool's core function, though it does not explicitly differentiate from sibling tools like get_events.

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?

The description provides no guidance on when to use this tool versus alternatives. It does not mention any preconditions, exclusions, or suggestions such as 'use get_events for live matches'. This leaves the agent without context 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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