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Lumify Sports Intelligence

by ai.lumify

get_player_events

Read-onlyIdempotent

List a player's events (schedule/results), paginated by id (after_id). Defaults to ±30 days around today when no date filter is given. Resolve player_id via search_players first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoEnd date YYYY-MM-DD.
fromNoStart date YYYY-MM-DD.
limitNoMax events to return per page.
statusNoFilter to events in this status.
after_idNoCursor: last event id from the previous page's next_after_id.
player_idYesPlayer id, from search_players.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNoEvents in the same shape as list_events' EventSummary.
has_moreNo
player_idNo
next_after_idNoPass as after_id to fetch the next page; null on the last page.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed5 schema fields changed
    • changedInput schema / properties / after_id / description
      Previous value: -"Cursor: last event id from previous page."New value: +"Cursor: last event id from the previous page's next_after_id."
    • addedInput schema / properties / limit / description
      Added value: +"Max events to return per page."
    • addedInput schema / properties / player_id / description
      Added value: +"Player id, from search_players."
    • addedInput schema / properties / status / description
      Added value: +"Filter to events in this status."
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "data": {
+      "description": "Events in the same shape as list_events' EventSummary.",
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "has_more": {
+      "type": "boolean"
+    },
+    "next_after_id": {
+      "description": "Pass as after_id to fetch the next page; null on the last page.",
+      "type": [
+        "integer",
+        "null"
+      ]
+    },
+    "player_id": {
+      "type": "integer"
+    }
+  },
+  "type": "object"
+}
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate read-only and idempotent. The description adds useful behavioral details: pagination by after_id and the default ±30-day date window when no filter is provided. No contradictions with annotations.

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?

Two concise sentences, front-loaded with the core action. Every clause adds value: scope, pagination, default window, and prerequisite. Zero waste.

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?

With a full output schema and rich annotations, the description doesn't need to detail return format or safety. It covers core behavior, pagination, defaults, and the player_id prerequisite, making it complete for the tool's complexity.

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?

Schema descriptions cover 100% of parameters, including player_id origin and after_id cursor semantics. The description reiterates these but doesn't add substantially new parameter-level information beyond connecting the pagination flow.

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 lists a player's events, with 'player's' distinguishing it from broader event tools. The parenthetical (schedule/results) and pagination mention add specificity. This is a specific verb+resource+scope, meeting the top criteria.

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

Usage Guidelines4/5

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

Provides clear context: it's for retrieving a specific player's events, and instructs to resolve player_id via search_players first. However, it does not explicitly contrast with alternative event-listing tools like list_events or query_events, so it lacks explicit exclusions or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Lumify Sports Intelligence toolsInspect
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TDQS

A4.4/5.0
Disambiguation5/5

Each tool maps to a distinct data resource or operation: events, live scores, odds, odds history, splits, stats, intelligence, player props, players, teams, sports, and seasons. Pairs like list_events vs query_events and get_event vs get_live_score are clearly differentiated by structured vs natural-language filtering and lightweight vs full detail.

Naming Consistency5/5

Tool names consistently follow a verb_noun snake_case pattern: get_*, list_*, search_*, query_*, batch_get_*, and estimate_cost. The naming conventions make the resource family immediately obvious, and deviations like batch_get_events are still predictable variants.

Tool Count4/5

19 tools is on the higher side, but each tool covers a specific sports-intelligence data product or workflow with little redundancy. The count feels intentional for the breadth of the domain rather than bloated.

Completeness4/5

The surface covers event discovery and retrieval, live scores, odds and line movement, splits, statistics, player props, intelligence, player/team/sport/season lookups, batch fetching, and cost estimation. Minor gaps like team standings or full rosters are not exposed, but core agent workflows are well supported.

Resources