list_leaguesList all available football / soccer leagues and seasons in OpenLigaDB (German football leagues like the Bundesliga, plus others). Returns league id, name, shortcut, season, and sport.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds value by specifying return fields (id, name, shortcut, season, sport) and scope (German leagues plus others). No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with action and resource, no unnecessary words. Perfectly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters, no output schema, and rich annotations, the description fully covers what an agent needs: what it returns and that it lists all leagues. Complete for this tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist; schema coverage is 100%. With zero parameters, baseline is 4. Description adds no parameter info because none are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'List all available football / soccer leagues and seasons', specifying the verb 'List' and the resource. It gives examples (e.g., Bundesliga) and distinguishes from sibling tools like get_matches or get_table, which deal with different data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you need a list of leagues. No explicit when-not or alternatives, but the tool has zero parameters and a clear purpose, so context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes, with detailed descriptions differentiating similar tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research. However, some overlap exists between ask_pipeworx and ask_pipeworx_beta, as both serve as universal routers with only minor routing improvements.
Tool names follow inconsistent patterns: some use snake_case (ask_pipeworx, entity_profile), others use lowercase single words (forget, recall), and some use camelCase (bet_research, deep_research). This mix of conventions makes the naming scheme unpredictable.
With 35 tools, the server covers a wide range of data sources, but the count feels slightly heavy for the apparent scope. Several tools serve meta-purposes (discover_tools, suggest_questions) or specialized functions (polymarket_arbitrage), adding to the complexity.
The tool set offers comprehensive coverage for financial, economic, drug, and news data, including comparison and grounding capabilities. However, the football-related tools are limited to German leagues, leaving a minor gap for other sports or regions.