get_tableGet the league table / standings for a football / soccer league and season (e.g. the Bundesliga table). Returns each team's position, played, won, draw, lost, goals, goal difference, and points.
| Name | Required | Description | Default |
|---|---|---|---|
| league | Yes | League shortcut, e.g. 'bl1' (Bundesliga), 'bl2' (2. Bundesliga), 'bl3' (3. Liga), 'dfb' (DFB-Pokal). | |
| season | Yes | Season start year, e.g. "2024". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety and behavior. The description adds value by detailing the return fields (position, played, won, draw, lost, goals, goal difference, points), which aids the agent in understanding expected output.
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?
The description is a single, well-structured sentence. It is front-loaded with the core purpose, includes an example, and lists return fields efficiently without unnecessary words.
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 the tool's low complexity (2 params, no output schema, simple data retrieval), the description is complete. It explains the output fields and provides an example, which compensates for the absence of an output schema.
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?
The input schema has 100% coverage with clear descriptions for both parameters, including examples for 'league'. The description adds minimal extra semantics beyond restating the purpose. Baseline 3 is appropriate as schema does the heavy lifting.
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?
The description uses specific verbs ('Get') and clearly states the resource ('league table / standings for a football / soccer league and season'). It includes an example (Bundesliga) and distinguishes from siblings like 'get_matches' or 'current_matchday' by focusing on standings 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 clearly indicates when to use the tool (to obtain league standings) and implicitly excludes other data types (matches, matchdays). While it does not explicitly state when not to use it, the purpose is distinct enough given the sibling tool names.
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.