A team's most recent archived results — opponent, outcome, and match event — for reading current form.
| Name | Required | Description | Default |
|---|---|---|---|
| team | Yes | team name, e.g. 'San Francisco Unicorns' | |
| limit | No | how many recent matches (default 8) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds that data is 'archived' and describes result content, but it does not clarify response ordering, pagination, or how open-world data is scoped. This is adequate but not rich behavioral disclosure.
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 compact sentence with the core subject 'team's most recent archived results' front-loaded and no filler. It earns its place by conveying both purpose and output content efficiently.
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 low-complexity read-only tool with two parameters, the description is largely complete: it names the resource, the recency scope, and the return fields (opponent, outcome, match event). There is no output schema, so a bit more detail about response structure or sort order would be helpful, but the gap is minor given the simple shape of the tool.
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%, with 'team' and 'limit' already explained. The description does not add parameter-level nuance; it describes the result fields rather than the parameters, so the baseline score of 3 is appropriate.
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 that the tool returns 'a team's most recent archived results' and lists the output content: opponent, outcome, and match event. The word 'archived' differentiates it from live-match tools, and the team-centric focus separates it from player, venue, and leaderboard siblings.
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 phrase 'for reading current form' gives a clear use context, so an agent can infer when this tool is appropriate. However, it does not explicitly name alternatives or state when not to use it, such as when a head-to-head comparison or live match data is needed.
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 distinguishable cricket data need: reference, match status, archives, player splits, team/venue stats, and probability. The only mild overlap is cricket_market_odds and cricket_win_probability, both returning probability-like output, but their descriptions make the market-vs-model distinction clear enough.
All tools share the cricket_ prefix and mostly use descriptive noun phrases such as cricket_player_career and cricket_venue_stats. cricket_explain_term breaks the pattern slightly as the only verb-led name, so the set is highly consistent but not perfectly uniform.
Eleven tools is well within the ideal range for a domain-specific cricket data server. Each tool covers a meaningful slice of the domain—explainer, live matches, archived scorecards, player/team/venue stats, leaderboards, and probabilities—without feeling bloated.
The surface covers most core cricket analytics workflows: lookup, live scores, career and phase stats, head-to-heads, team form, venue behavior, leaderboards, and win probability. Minor gaps exist such as detailed live ball-by-ball commentary or series-level schedules, but agents can generally accomplish common cricket questions without dead ends.