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Crawlora MCP

Crawlora MCP

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by Crawlora-org
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JavaScript
Remote

nba_player_awards

Retrieve a player's NBA award history by player ID, returning one row per award with season, team, conference, and award type details.

Instructions

NBA player award history. Returns one player's award history, one row per award. Marker-verified columns are PERSON_ID, FIRST_NAME, LAST_NAME, TEAM, DESCRIPTION, SEASON, CONFERENCE, TYPE, SUBTYPE1, SUBTYPE2, SUBTYPE3, ALL_NBA_TEAM_NUMBER and ALL_NBA_TEAM; the sampled AWARD_TYPE, ALL_DEFENSIVE_TEAM_NUMBER and ALL_ROOKIE_TEAM_NUMBER names did not match and are not claimed.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "player_id": "<player_id>"
    },
    "name": "nba_player_awards"
  }
}

Input Schema

NameRequiredDescriptionDefault
player_idYesPlayer id from nba-players or nba-team-roster

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.20

TDQS

A3.6/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does add real behavioral context: the result grain (one row per award) and, unusually, a data-quality caveat that certain column names (AWARD_TYPE, ALL_DEFENSIVE_TEAM_NUMBER, ALL_ROOKIE_TEAM_NUMBER) did not verify against samples and are not claimed. It does not cover failure behavior for an unknown player_id or result ordering, which keeps it below a 5.

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

Conciseness3/5

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

Purpose and grain are front-loaded in the first sentence, which is good. The remainder is a dense comma-separated column enumeration that reads like schema detail rather than selection guidance, making the description list-heavy relative to its informational payload.

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

Completeness4/5

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

There is no output schema, so documenting the returned columns is genuinely necessary, and the description does that along with the grain and the caveat about unverified columns. What remains missing is minor: ordering, and what happens when a player has no awards.

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 coverage is 100% and the single parameter already documents its provenance ('Player id from nba-players or nba-team-roster'). The description adds no further semantics about the parameter, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: returns one player's NBA award history, with the grain explicitly declared as one row per award. The scope (award history for a single player) is distinct from the stat/log-oriented siblings, though no sibling is named to sharpen the boundary.

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?

Usage is only implied: 'one player's award history' plus a required player_id signals a single-player lookup. There is no explicit when-to-use guidance, no statement about when a different tool (e.g. career stats or bio) is the better choice, and no prerequisites or exclusions.

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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