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Glama

Espn

by io.github.pipeworx-io

Get Teams

get_teams
Read-onlyIdempotent

List all teams (rosters/franchises) for a league: names, abbreviations, locations, and colors. Works for NFL/NBA/MLB/NHL/soccer/college. 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'.

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"
+  },
+  {
+    "league": "eng.1",
+    "sport": "soccer"
+  }
+]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

The description aligns with annotations (readOnlyHint, idempotentHint) and adds value by disclosing the output fields (names, abbreviations, locations, colors) and supported leagues. No contradictions or missing behavior details.

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 efficiently structured: a single sentence stating the purpose and output, followed by a second sentence listing supported leagues and common pairs. Every sentence adds value, and the information is front-loaded.

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?

Given no output schema, the description explicitly lists the returned fields and covers the supported sports and leagues comprehensively. It provides enough detail for the agent to understand the tool's scope and 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 schema covers both parameters with brief descriptions, but the description adds significant value by providing multiple concrete examples of valid sport/league pairs (e.g., 'football/nfl', 'soccer/eng.1'), which aids parameter selection.

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 verb 'List', the resource 'teams', and the scope 'for a league'. It specifies the returned fields (names, abbreviations, etc.), making the purpose unambiguous. Although it doesn't explicitly distinguish from siblings like get_standings, the purpose is distinct enough.

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 provides clear usage context by listing supported sports and common sport/league pairs, which helps the agent select appropriate parameters. However, it does not explicitly state when to use this tool versus alternatives like get_team_schedule.

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