Get college baseball scores and game results returned by the configured providers. Includes team names, scores, venue, game status, source, and fetch time.
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | Date in YYYY-MM-DD format. Defaults to today (America/Chicago). | |
| conference | No | Filter by conference (e.g., "SEC", "Big 12", "ACC"). Optional. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior, so the bar is lower. The description adds useful context about 'configured providers' and includes 'source and fetch time' in the output, which helps the agent understand the data's provenance and freshness. It does not contradict annotations.
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 that front-loads the purpose ('Get college baseball scores') and then lists what is included. Every word earns its place, with no filler.
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 relatively simple with no output schema and only two optional parameters. The description adequately lists the output fields and implies the tool returns aggregated scores. It doesn't explicitly state the default date behavior, but that is in the schema. For this complexity level, the description is nearly complete.
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 100% for both optional parameters (date and conference), so the schema carries the parameter documentation. The description does not repeat or enhance parameter semantics beyond what the schema already provides, which is acceptable given the high coverage.
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 'Get' with a specific resource: 'college baseball scores and game results.' It lists concrete data fields (team names, scores, venue, game status, source, fetch time) and the tool name 'get_scoreboard' aligns with an aggregated scoreboard, distinguishing it from siblings like 'get_match_detail' or 'get_team_schedule.'
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 for fetching scoreboard data for a date, but it does not explicitly state when to use this tool versus alternatives like 'bsi_get_match_detail' for a single game or 'bsi_get_team_schedule' for a team's schedule. No exclusions or alternative guidance is provided, leaving the agent to infer from the tool name.
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 targets a distinct resource: game details, player stats, rankings, scoreboard, standings, team schedules, and web search. There is no overlap between these purposes, and the descriptions make the boundaries clear.
Six tools follow a consistent 'bsi_get_<resource>' pattern, while 'bsi_search_intel' deviates by using 'search' instead of 'get'. This is a minor inconsistency but still readable and predictable overall.
With 7 tools, the set is well-scoped and each tool serves a distinct function. The count is appropriate for a sports information server, not too thin or overbearing.
The tool set covers major aspects of college baseball: games, players, rankings, live scores, standings, schedules, and narrative context via web search. No significant gaps are apparent, and the search tool provides a fallback for any uncovered queries.
