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DanielTomaro13

sportsdata-mcp

laliga_squad

Read-onlyIdempotent

Retrieve a club's current La Liga squad, including player details, photos, positions, shirt numbers, and loan status. Requires team slug and subscription slug.

Instructions

A club's CURRENT squad — each entry has person (name, date_of_birth, country, height), photos, position id, shirt_number, opta_id, loan status. (The subscription arg is required by the API but the roster returned is the current one, not season-historical.)

Returns: {total, squads:[{id, opta_id, person:{name, date_of_birth, country, height}, photos, position, shirt_number, role, current, loan}]}

Auth: works without a key; LALIGA_SUBSCRIPTION_KEY unlocks more if set.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesTeam slug (e.g. real-madrid). Required — part of the URL path.
subscriptionYesSubscription slug (required by the API; e.g. laliga-easports-2025).
Behavior4/5

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

Annotations already state readOnlyHint, openWorldHint, and idempotentHint. The description adds value beyond these by disclosing auth behavior ('works without a key; LALIGA_SUBSCRIPTION_KEY unlocks more if set') and clarifying that the required subscription parameter does not change the current-roster scope. It also provides the return structure, which is useful context.

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 compact and well-organized: a definition of the returned data, a return-format structure, and an auth note. Each sentence is purposeful, with no redundancy or filler, making it easy for an agent to parse quickly.

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

Completeness5/5

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

For a read-only tool with only two parameters and no output schema, the description covers everything needed: what the tool returns (with nested fields), the required parameters' roles, auth requirements, and a clarification about the subscription argument. It is complete for an agent to select and invoke correctly.

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?

Schema coverage is 100% for both parameters, so baseline is 3. The description adds semantic value by explaining that the subscription argument is required but does not affect the historical/current scope, and it clarifies the slug with an example in the schema. This extra context goes beyond the bare parameter names.

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 'A club's CURRENT squad' with a list of player attributes (person, photos, position, shirt_number, etc.). It distinguishes from season-historical data by explicitly noting the roster is current, not historical, and the tool name 'laliga_squad' aligns with this purpose.

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

Usage Guidelines3/5

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

The description implies usage by emphasizing 'CURRENT squad' and clarifying the subscription parameter, but it does not explicitly mention when to use this tool over sibling tools like laliga_team or laliga_matches. No alternatives are named or exclusions given, leaving usage guidance implicit rather than explicit.

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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