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i-m-arul

CricketStudio MCP

by i-m-arul

get_related_entities

Traverse the cricket knowledge graph to find entities linked to a player or franchise. Filter by predicate (plays_for, faced, dismissed_by) and direction to answer relationship queries.

Instructions

Knowledge-graph traversal: entities connected to a player or franchise (by slug). Use for "who does Kohli play for", "which bowlers has Kohli faced", "who plays for RCB". Optional predicate filters the edge type (plays_for, faced, dismissed_by) and direction (out/in/both). Returns related entities + canonical URLs. Does NOT return per-ball detail — use get_player_h2h for one matchup. Matchup edges mirror the get_player_h2h pair set.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesEntity slug, e.g. "virat-kohli" or "rcb"
limitNoDefault 25, max 50
directionNo
predicateNo
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses return value ('related entities + canonical URLs') and what it excludes (per-ball detail), but does not explicitly state that the operation is read-only or safe to call repeatedly. Since no annotations are provided, this omission leaves ambiguity about behavioral traits like side effects or permissions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph of three sentences, front-loaded with core purpose and examples, followed by parameter details and exclusions. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description covers purpose, parameters, usage, and exclusions adequately. It could mention whether results are paginated or if limit is exact, but for a graph traversal tool the information is sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 50%, so baseline is 3. The description adds context by explaining that `slug` identifies the entity, `predicate` filters edge types (listing the three values), and `direction` controls traversal direction. It also explains `limit` default and max. This compensates for missing schema descriptions for `direction` and `predicate`.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it performs 'knowledge-graph traversal' to find entities connected to a player or franchise, with concrete examples like 'who does Kohli play for'. It distinguishes itself from get_player_h2h by explicitly noting that tool is for per-ball detail.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit usage guidance: examples of queries it answers, and a clear when-not-to-use instruction: 'Does NOT return per-ball detail — use get_player_h2h for one matchup.'

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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