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sportiq-mcp

by io.github.Ninjabeam20

cricket_head_to_head

Read-onlyIdempotent

Compare two cricket teams head-to-head using squad form and player stats.

Args: team_a: First team code or name (e.g. "MI", "India"). team_b: Second team code or name (e.g. "CSK", "Australia").

Returns: data: {team_a, team_b, team_a_edge_count, team_b_edge_count, key_players_a, key_players_b, h2h_win_rate_a, h2h_win_rate_b, win_prob_a, win_prob_b}. meta.estimated: true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
team_aYesFirst team code or name (e.g. "MI", "India").
team_bYesSecond team code or name (e.g. "CSK", "Australia").

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

A4.2/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds value by stating that results are estimates via 'meta.estimated: true' and by listing the computed output fields such as edge counts and win probabilities.

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?

The description is well-structured with a front-loaded purpose statement, followed by Args and Returns sections. It is reasonably concise, though the Args section is redundant with the input schema and could be trimmed.

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 the simple two-parameter interface, read-only annotations, and output schema availability, the description is complete. It explains what the tool does, what inputs are needed, and what outputs to expect, including the estimated-result flag.

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 both parameters are already documented with examples in the input schema. The description's Args section duplicates that information without adding extra semantics like accepted code formats, case sensitivity, or ordering constraints.

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's verb and resource: 'Compare two cricket teams head-to-head'. It is specific to cricket team comparison, which distinguishes it from player-level, football, and F1 sibling tools.

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 first sentence gives clear context for when to use the tool: comparing two cricket teams using squad form and player stats. However, it does not explicitly mention alternative tools or state when not to use it, so it falls short of a perfect score.

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.

Resources