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asd-git-master

AltSportsLeagues MCP Server

discover_leagues

Discover alternative sports leagues by describing your search in natural language. Apply optional filters for sport type, region, and more.

Instructions

Search for alternative sports leagues using AI-powered discovery.

Accepts natural language queries like "combat sports in Southeast Asia" or "emerging racing leagues." Optionally pass structured filters for sport type, region, minimum fan count, etc.

Returns a ranked list of matching leagues with summary profiles.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language search query describing the leagues you want.
filtersNoOptional dict of structured filters (sport, region, min_fans, etc.).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

Discloses that it returns a ranked list with summary profiles, implying read-only behavior. No mention of required permissions, rate limits, or potential side effects. With no annotations, the description carries the burden, and it meets minimum expectations but lacks depth.

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?

Three sentences, front-loaded with the main purpose. Examples are helpful but slightly increase length. Overall efficient without redundancies.

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

Completeness3/5

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

With an output schema (not shown) and description stating 'ranked list with summary profiles,' return value is adequately described. However, given many sibling tools, more context on how this differs from search_similar_leagues or query_leagues would improve completeness.

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 coverage is 100%, so baseline is 3. Description adds examples for the query parameter and mentions filter types (sport, region, min_fans), which provides some additional context but does not detail each filter field or their formats.

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?

Clearly states it searches for alternative sports leagues using AI-powered discovery, with specific verb and resource. Distinguishes from siblings like query_leagues or search_similar_leagues by emphasizing natural language and alternative focus, but could be more explicit about uniqueness.

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?

Provides examples of natural language queries and mentions optional filters, which gives basic usage context. However, no explicit guidance on when to use this tool versus siblings like list_discovered_leagues or search_similar_leagues, and no conditions for when not to use it.

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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