List World Cup 2026 matches. Use filter='to_predict' to find upcoming matches with confirmed teams that are open for predictions.
| Name | Required | Description | Default |
|---|---|---|---|
| filter | No | 'upcoming' = future matches, 'to_predict' = upcoming confirmed-team matches open for prediction that you (via X-API-Key) haven't already predicted, 'past' = finished, 'today' = today's UTC matches, 'all' = everything. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It explains that 'to_predict' filters based on the user's previous predictions, which is a key behavior. However, it omits details like ordering, pagination, or whether the list is exhaustive.
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?
Two sentences, no redundancy. The first states the purpose, the second provides actionable guidance. Every word earns its place.
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?
The tool is simple with one optional parameter and no output schema. The description covers the main functionality and a filter use case, but does not mention return format, field details, or pagination. For a list tool, this is a minor gap.
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?
Schema coverage is 100% and the schema already provides detailed descriptions for all enum values. The tool description minimally adds context by rephrasing the 'to_predict' option, so it adds little beyond the schema.
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 clearly states the tool lists World Cup 2026 matches, which is a specific verb+resource. It distinguishes from sibling tools (get_leaderboard, submit_prediction) which serve different purposes.
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 explicitly recommends using filter='to_predict' for a key use case (upcoming confirmed matches open for predictions). While it doesn't cover all filter options, the single filter guidance is valuable for the agent.
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.
Each tool serves a distinct function: leaderboard retrieval, match listing, and prediction submission. No overlap in purpose.
All tools follow a consistent verb_noun pattern: get_leaderboard, list_matches, submit_prediction.
Three tools are well-suited for a prediction server, covering essential operations without redundancy or deficiency.
Core workflows (viewing matches, submitting predictions, seeing leaderboard) are covered. Minor gaps exist, such as no tool to view personal prediction history or get match details by ID.