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Espn

by io.github.pipeworx-io

Get Scoreboard

get_scoreboard
Read-onlyIdempotent

Live sports scores: NFL/NBA/MLB/NHL/soccer/college scores for today's games and results. Returns each game with teams, scores, status (live/final), clock, and period. 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
datesNoOptional YYYYMMDD date filter. Default = today's games.
sportYesSport, e.g. 'football', 'basketball', 'baseball', 'hockey', 'soccer'.
leagueYesLeague slug, e.g. 'nfl', 'nba', 'mlb', 'nhl', 'eng.1'.

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": "nfl",
+    "sport": "football"
+  },
+  {
+    "dates": "20250115",
+    "league": "nba",
+    "sport": "basketball"
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already indicate readOnly, idempotent, and non-destructive. Description adds value by specifying return fields (teams, scores, status, clock, period) and the scope (today's games and results). No contradictions.

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?

Core purpose is front-loaded. The list of sport/league pairs is somewhat lengthy but informative. No wasted sentences, though could be slightly more concise.

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?

No output schema exists, but description clearly states what fields are returned. For a straightforward data retrieval tool, it provides sufficient context about output and behavior.

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 covers 100% of parameters with basic descriptions. The description adds extensive common sport/league pairs, which aids agent in parameter selection beyond schema.

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?

Explicitly states it retrieves live sports scores for multiple leagues and lists supported sports. Clearly distinguishes from sibling tools like get_standings and get_team_schedule.

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?

Provides common sport/league pairs but lacks explicit guidance on when to use this tool versus alternatives. No when-not or exclusions mentioned.

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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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