How a player performs by phase of the innings — powerplay, middle overs, and the death — for batting and bowling. This is the split that separates a strike-rate merchant from a genuine finisher, computed from ball-by-ball archives.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | player name, e.g. 'Nicholas Pooran' | |
| total_overs | No | 20 for T20 (default), 50 for ODI/List-A |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds a useful data-source detail ('computed from ball-by-ball archives') but does not disclose player-name matching behavior, data availability, or behavior for unsupported formats. No contradiction with 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 first sentence is concise and front-loads the core purpose. The second sentence is rhetorical flavor about separating 'a strike-rate merchant from a genuine finisher' and does not help an agent select or invoke the tool, so not every sentence 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 two parameters and strong schema coverage, and the core query intent is clear. However, there is no output schema and the description does not indicate what metrics or fields the result will contain, leaving some uncertainty about the returned shape.
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 input schema already documents both parameters with 100% description coverage, so the baseline is 3. The tool description adds no parameter-specific detail beyond the schema, though it does clarify the batting/bowling split that the player name parameter relates to.
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 identifies a player-performance stats tool broken down by innings phase (powerplay, middle overs, death) for both batting and bowling. It lacks a direct imperative verb like 'retrieve' or 'calculate', and it doesn't explicitly distinguish itself from sibling player-oriented tools, but the phase-split scope is specific and recognizable.
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 gives no guidance on when to use this tool over cricket_player_career, cricket_team_form, or cricket_venue_stats. It only mentions that the data is computed from ball-by-ball archives, which implies historical analysis but does not help an agent choose between alternatives.
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.