Career batter-vs-bowler record from ball-by-ball archives: balls faced, runs scored, dismissals, strike rate. Cricket tracks these like baseball's batter-vs-pitcher splits.
| Name | Required | Description | Default |
|---|---|---|---|
| batter | Yes | batter name, e.g. 'Virat Kohli' or 'V Kohli' | |
| bowler | Yes | bowler name, e.g. 'Jasprit Bumrah' | |
| format | No | 't20' or 'odi'; omit to try T20 then ODI |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations cover read-only and non-destructive behavior. The description adds useful context beyond annotations by identifying the data source ('ball-by-ball archives') and the nature of the data (career-level record), which helps set expectations about scope and granularity.
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?
Two tight sentences: the first states the purpose and output fields, the second gives a useful analogy for familiarity. No filler or repetition; every clause 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?
For a simple read-only lookup with fully documented parameters, the description is sufficient. It names the output metrics and data source, and the schema covers the format defaults. A caveat about missing/insufficient ball-by-ball data could add completeness, but is not essential for correct invocation.
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 batter, bowler, and format all documented. The description reinforces the head-to-head nature but does not add parameter-specific detail beyond the schema, so the baseline 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 states a specific resource ('Career batter-vs-bowler record') and enumerates the exact metrics returned (balls faced, runs scored, dismissals, strike rate), making it immediately clear what the tool does and how it differs from general player career or phase stats tools.
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 batter-vs-bowler head-to-head queries, but it does not state when to prefer it over siblings like cricket_player_career, cricket_phase_stats, or cricket_match_archive. No explicit usage context or exclusions are provided.
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.