get_team_scheduleUpcoming games for a team (dates, opponent, venue).
| Name | Required | Description | Default |
|---|---|---|---|
| team | Yes | Team name, nickname, slug, or abbreviation - e.g. 'Chiefs', 'newyorkknicks', 'NYK' |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the read-only, idempotent, non-destructive nature of the tool, so the description's burden is lower. It adds useful context about the returned games' attributes (dates, opponent, venue), but offers no details on ordering, time range, pagination, or behavior for unknown teams.
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 front-loaded sentence with no wasted words, clearly conveying the resource and the key returned fields. It is appropriately 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?
For a simple one-parameter, read-only schedule lookup, the description covers the essential output shape and is supported by clear annotations. The main gaps are minor—ordering, timezone, or exact date range of 'upcoming'—but the tool is simple enough that the description is adequate.
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 schema already documents the 'team' parameter thoroughly with examples of accepted formats, so schema coverage is 100%. The description adds no additional parameter-level semantics beyond referring to 'a team.'
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 title and description make the tool's purpose obvious: retrieve upcoming games for a team, listing dates, opponent, and venue. It is clearly distinguishable from the sibling tools, which cover injuries, odds, player stats, rosters, and team lists.
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 this tool is for upcoming schedule information, but it does not explicitly state when to use it over alternatives or what it excludes, such as past games or full season schedules. Usage context is clear but not fully specified.
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: injuries, odds, player stats, roster, schedule, and the universe of teams. There is no overlap or ambiguity between them.
All tools use a clear verb_noun pattern: get_* for specific resources and list_teams for the full collection. This is consistent and predictable.
Six tools is well-scoped for a read-only sports data server covering NFL and NBA information. Each tool covers a meaningful slice of the domain without bloat.
The surface covers the main pre-game and season data needs: teams, rosters, schedule, stats, injuries, and odds. Only a minor gap exists for something like game results or standings, which is not central to the apparent betting/team-info focus.