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sofascore_event_shotmap

Retrieve every shot from a football or ice hockey match by event id, with shooter, outcome, xG and pitch/rink coordinates. Analyze shot maps and match shot data.

Instructions

SofaScore event shot map. Returns every shot in a football or ice hockey match. Football shots carry the shooter, side, outcome (shot_type), situation, body part, expected goals (xG and xGOT), minute, and pitch and goal-mouth coordinates. Ice hockey shots carry the shooter, team, side, shot technique (shot_type, for example wrist), outcome (made, saved, missed or blocked), strength (even, powerplay or shorthanded), period and period clock, rink coordinates and, for most shots, xg. An empty shots list is a valid response (a live match with no shots yet). Returns 404 when SofaScore has no shot map for the match, which includes matches that have not started, basketball and other sports without one, and fixtures without expected-goals coverage.

Input Schema

NameRequiredDescriptionDefault
idYesNumeric SofaScore event (match) id

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.12

TDQS

A4.2/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so well: it discloses that an empty 'shots' list is a valid live-match response and enumerates the precise 404 conditions (not started, unsupported sports like basketball, no xG coverage). This is exactly the edge-case context an agent needs to avoid misreading a failure.

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

Conciseness4/5

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

Front-loaded with the core purpose, and the long field enumeration is justified because there is no output schema. It is a dense single block and slightly verbose, but nearly every clause conveys usable field or edge-case information.

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

Completeness5/5

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

For a one-parameter read tool with no output schema, the description fully documents the return shape (football and hockey field sets), the valid empty response, and the failure modes. Nothing an agent needs in order to call and interpret this tool is missing.

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

Parameters3/5

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

Only one parameter exists and schema coverage is 100%, with the schema already describing 'id' as the numeric SofaScore event id. The description adds no syntax or format detail beyond that, so the baseline 3 applies.

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?

States a specific verb and resource ('Returns every shot in a football or ice hockey match') and immediately scopes the tool to a shot map. It goes further by enumerating the returned fields for both sports, so an agent knows exactly what resource this exposes versus the aggregate-stat sibling tools.

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

Usage Guidelines3/5

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

The description implies usage through scope (shot-level data, football and ice hockey only) and explains the empty-list and 404 situations, but it never names an alternative tool or states when to prefer this over e.g. event statistics or lineups. Usage is inferable but not explicitly routed.

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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