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sportiq-mcp

by io.github.Ninjabeam20

cricket_get_scorecard

Read-onlyIdempotent

Return the full scorecard for a specific match.

Args: match_id: The match identifier (e.g. from cricket_get_live_matches).

Returns: data: full scorecard with innings, partnerships, bowling figures. meta.source: adapter that served the data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
match_idYesThe match identifier (e.g. from cricket_get_live_matches).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
metaNo
errorNo

Schema Changelog

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

  1. First observed

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already establish read-only, idempotent, non-destructive behavior, lowering the burden. The description adds useful context about the scope of 'full scorecard' and the meta.source adapter field, but does not go deeper into data freshness or error behavior. This is on par with the calibrated baseline for annotation-carrying tools.

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 concise and well-structured with clear Args and Returns sections. The core purpose is front-loaded in a single sentence, and every listed item adds useful information without filler or redundancy.

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?

The tool takes a single required parameter, has 100% schema coverage, an output schema, and strong safety annotations. The description supplies the only missing workflow detail—how to source the match_id—making it fully complete for an agent to invoke correctly.

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 description coverage is 100%, and the tool description repeats the same match_id explanation (including the reference to cricket_get_live_matches). The description does not add any semantic detail beyond what the input schema already provides, so the baseline score of 3 is appropriate.

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?

Description opens with 'Return the full scorecard for a specific match', a specific verb and resource, and the Returns section details exactly what a scorecard contains (innings, partnerships, bowling figures). This clearly distinguishes it from siblings like cricket_get_live_matches, cricket_get_squad, and cricket_get_pitch_report.

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 a clear prerequisite by noting the match_id is obtained from cricket_get_live_matches, which establishes when this tool is appropriate. It does not explicitly name alternatives or exclusion cases, but the narrow scope of 'scorecard for a specific match' makes those unnecessary.

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

A3.6/5.0
Disambiguation3/5

Sport prefixes make the three domains easy to separate, and most tools have clear purposes. However, several tools overlap: football_match_predictor vs football_xg_model, f1_head_to_head_pace vs f1_race_pace_compare, and the cricket fantasy tools (build_dream11, captain_recommendation, differential_picks) all require careful reading to avoid misselection.

Naming Consistency4/5

The sport prefix + snake_case pattern is consistent and retrieval tools uniformly use get_, which creates predictability. The main deviation is that many analysis/model tools are noun phrases rather than verb_noun (cricket_head_to_head, football_knockout_path, f1_tyre_degradation), but they remain readable and scoped.

Tool Count2/5

44 tools is well above the 25+ threshold for a single server and will strain agent context and tool-selection quality. Each sport block is individually reasonable at 13-15 tools, but combining three sports plus cross-sport and health utilities makes the overall surface too large.

Completeness3/5

Core workflows are broadly covered: live data, schedule/standings, match prediction, tournament simulation, fantasy help, and strategy analysis exist for each sport. However, there are notable dead ends: cricket_player_form_index needs a player_id no exposed tool returns, football_get_match_stats requires an API-Football numeric ID not mapped anywhere, and f1_get_lap_times references a stints endpoint that is not exposed as a tool.

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