How a ground plays: matches recorded, average first-innings score, highest first-innings total, and how often the chasing side wins there. Useful for toss decisions and pre-match reads.
| Name | Required | Description | Default |
|---|---|---|---|
| venue | Yes | ground name or fragment, e.g. 'Grand Prairie' or 'Eden Gardens' | |
| 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 read-only, non-destructive behavior. The description adds useful behavioral context by enumerating the key stats returned and framing them as historical venue tendencies. It does not explain output formatting or edge cases, but the core read-only behavior is reinforced rather than contradicted.
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 two sentences with no filler. It front-loads the core concept ('How a ground plays'), lists the meaningful stats, and ends with a practical use case. 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?
For a simple read-only stats tool with only one required parameter and full schema coverage, the description is complete. It tells the agent what the tool computes, why it matters, and how to apply the results. No output schema exists, but the described metrics give a sufficient mental model of the result.
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%, so the schema already documents both parameters well, including the venue example and the total_overs defaults. The description adds no new parameter-level detail, but the baseline of 3 is appropriate because the schema carries the necessary semantic load.
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 the resource ('a ground') and the specific purpose: summarizing how a venue plays. It lists concrete outputs (matches recorded, average first-innings score, highest total, chasing-side win rate), which makes the tool's function unambiguous and distinct from the sibling tools like head-to-head or team form.
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 states when to use this tool: 'for toss decisions and pre-match reads.' This gives the agent a clear context for invocation. It does not explicitly name alternative tools or when-not-to-use conditions, but the use case is well implied by the venue-specific scope.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.