Call this to fetch real-time sports game statistics, play-by-play data, and live event feeds from Kalshi.
| Name | Required | Description | Default |
|---|---|---|---|
| data_type | No | ||
| event_ticker | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only states 'fetch,' implying a read operation, but it does not mention authentication requirements, rate limits, or potential side effects. The description is insufficient for a tool with no annotation coverage.
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, concise sentence (18 words) that front-loads the action and resource. It is not verbose, but it could be more structured to include parameter hints or usage notes. Still, it earns its place with no wasted 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 has two parameters with no descriptions and no guidance on valid values, the description is incomplete for effective use. The presence of an output schema covers return format, but the description does not explain how to specify what data to fetch, leaving the agent under-informed.
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 description coverage is 0%, and the description does not add any meaning to the two parameters ('data_type' and 'event_ticker'). Although the description hints at sports data, it fails to explain what values the parameters accept or how they map to the data types. The description must compensate for the lack of schema descriptions but does not.
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 verb 'fetch' and the resource 'real-time sports game statistics, play-by-play data, and live event feeds from Kalshi.' It distinguishes the tool from siblings by focusing on live data, which is not covered by other tools like foresea_forecast or foresea_price_history.
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 provides a clear context for when to use the tool (when needing real-time data), but it does not explicitly mention when not to use it or suggest alternatives. The agent must infer from sibling tool names, lacking explicit guidance.
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.
Some tools have overlapping purposes: foresea_edge_board and foresea_scan_markets both return markets ranked by model-vs-market edge, and foresea_forecast vs foresea_debate_market both provide probabilities for a question. Descriptions help differentiate (specific vs. broad, quick vs. deep) but the boundaries are not always clear.
All tools share the 'foresea_' prefix and use lowercase with underscores, which provides consistency. However, the remainder mixes verb-noun phrases (analyze_market, scan_markets) with bare nouns (orderbook, price_history) and compound nouns (market_leaderboard, polymarket_meta), creating minor stylistic inconsistency.
17 tools is slightly above the typical well-scoped range of 3-15, but the server covers a broad domain (forecasting, market analysis, data feeds, portfolio optimization, track record). A couple of tools are redundant (edge_board vs scan_markets) and could be consolidated, but overall the count is reasonable.
The tool set covers the core analytics workflow: general forecasting, specific market analysis, opportunity scanning, market data (quotes, orderbook, history, trades), portfolio allocation, and performance evaluation. Minor gaps exist (e.g., no tool for detailed market rules or user portfolio tracking), but these are likely outside the intended scope.
