Explain a cricket term in plain English with its closest baseball equivalent (wicket, yorker, googly, powerplay, DLS, and ~60 more).
| Name | Required | Description | Default |
|---|---|---|---|
| term | Yes | cricket term, e.g. 'googly' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description goes beyond that by revealing the response style ('plain English'), the comparative output ('closest baseball equivalent'), and the scope ('~60 more' terms). It doesn't cover unknown-term behavior, but the annotations carry the safety profile and the added context is useful.
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?
A single, front-loaded sentence that states the action, output format, and scope with no filler. Every part of the sentence earns its place, and the examples improve comprehension without bloat.
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, single-parameter lookup tool, the description is complete: it explains what the tool does, what kind of output to expect, and gives a sense of the covered terms. Annotations cover side effects, sibling context disambiguates from stats tools, and no output schema is necessary for such an explanatory response.
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% for the single parameter, so the schema already fully documents 'term'. The description reinforces it with examples and scope, but does not add meaningful new semantics beyond what the schema provides. Baseline 3 applies.
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 ('Explain') and a clear resource ('a cricket term'), and adds a distinctive output feature ('closest baseball equivalent'). It also lists concrete examples, making the tool's purpose immediately distinguishable from the statistical sibling 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 makes the use case clear: this is a terminology-explanation tool, not a stats or data tool. It does not explicitly name alternatives or exclusions, but the sibling list is all data/analytics tools, so the contrast is strong and the intended usage is evident.
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.