Skip to main content
Glama

Espn

by io.github.pipeworx-io

Get Team Schedule

get_team_schedule
Read-onlyIdempotent

A team's schedule — upcoming and recent games with dates, opponents, and (for finished games) scores and result. PREFER for "when do the Lakers play next", "'s schedule", " recent results". Get the numeric team_id from get_teams. Common sport/league pairs: football/nfl, football/college-football, basketball/nba, basketball/wnba, basketball/mens-college-basketball, baseball/mlb, hockey/nhl, soccer/eng.1 (Premier League), soccer/usa.1 (MLS), soccer/esp.1 (La Liga), soccer/uefa.champions (Champions League).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sportYesSport, e.g. 'basketball'.
leagueYesLeague slug, e.g. 'nba'.
team_idYesNumeric ESPN team id (from get_teams), e.g. "13" for the Lakers.

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "league": "nba",
+    "sport": "basketball",
+    "team_id": "13"
+  },
+  {
+    "league": "nfl",
+    "sport": "football",
+    "team_id": "25"
+  }
+]
  2. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate read-only and idempotent behavior. The description adds that the output includes scores and results for finished games, providing context beyond annotations without contradiction.

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 extremely concise with two sentences and a list. It front-loads the core purpose and includes only essential information, with no wasted words. The list of sport/league pairs is useful and efficiently presented.

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 three required parameters and no output schema, the description explains what the output contains (dates, opponents, scores for finished games) and references a prerequisite tool (get_teams). This provides sufficient context for correct usage.

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?

The input schema has 100% description coverage, but the description reinforces that team_id comes from get_teams and gives common sport/league pairs, adding practical value beyond schema definitions.

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 the tool returns a team's schedule with upcoming and recent games, including dates, opponents, and scores for finished games. It uses specific verbs ('schedule', 'games') and resources ('team schedule') and distinguishes from siblings like get_scoreboard and get_standings.

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?

The description explicitly suggests using this tool for queries like 'when do the Lakers play next' and provides guidance on getting team_id from get_teams. It also lists common sport/league pairs, demonstrating clear usage context though it doesn't explicitly state when not to use it.

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

All Espn toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation3/5

Several tools cluster around similar purposes—the three ask_pipeworx variants, the five polymarket_* analysis tools, and the meta/discovery tools (discover_tools, suggest_questions, pipeworx_trending)—so an agent could plausibly call the wrong one. However, the descriptions are exceptionally detailed with explicit 'use this when' guidance, which mitigates most confusion.

Naming Consistency3/5

Names are almost all snake_case, but there is no consistent verb_noun or resource_action pattern: ask_pipeworx, entity_profile, remember, get_scoreboard, polymarket_fill_risk, etc. Pairs like remember/recall/forget and subscribe/unsubscribe are consistent, but the broader set mixes verbs, nouns, and prefixes (ask_, pipeworx_, polymarket_, get_, scan_) without a unified scheme.

Tool Count2/5

36 tools is well over the 25-tool 'heavy' threshold, and the set spans multiple unrelated domains: sports data, a general structured-data router, prediction-market analysis, memory storage, and user feedback. Many tools are meta or auxiliary (suggest_questions, pipeworx_feedback, remember/recall/forget) that don't clearly belong to the server's core purpose, making the set feel bloated.

Completeness3/5

For a sports-data server, core score/news/standings/team/schedule operations exist, but player stats, game details, injuries, and playoff brackets are missing. For the broader Pipeworx data platform the surface is extensive, but the mix of domains makes it hard to declare the set complete for any single stated purpose.

Resources