Look up an archived limited-overs match and return its scorecard: innings totals, top scorers, leading wicket-takers. Search by team names, league (mlc, ipl, bbl, psl, cpl), and/or year.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | free text, e.g. 'the 2025 IPL final' or 'Washington Freedom vs San Francisco Unicorns 2026' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds useful behavioral detail about the lookup scope and the returned data, including that it searches by team names, league, and year, which goes beyond the 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?
Two concise sentences, with the core action and return value front-loaded. No filler or repetition, and every phrase contributes to selection and invocation.
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?
With no output schema, the description still explains the return value (scorecard components) and the acceptable search inputs. It is adequate for a simple one-parameter lookup, though a note on the absence of Test matches or the meaning of 'limited-overs' could marginally improve completeness.
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 has 100% coverage for the single free-text query parameter, so the baseline is already solid. The description adds meaning by specifying the search dimensions: team names, league, and/or year, which clarifies how the free-text query should be composed.
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 uses a specific verb ('Look up') with a clear resource ('archived limited-overs match') and states the concrete return payload ('scorecard: innings totals, top scorers, leading wicket-takers'). It clearly distinguishes this from live-match and aggregate-stat 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 description gives clear context for when to use the tool: searching archived limited-overs match scorecards by team, league, or year. It does not explicitly name alternative tools or state when not to use it, but the 'archived' qualifier and focus on match scorecards provide a practical usage boundary.
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.