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sportiq-mcp

by io.github.Ninjabeam20

f1_undercut_window

Read-onlyIdempotent

Estimate whether an undercut is viable for the attacker against the target.

Args: session_key: OpenF1 session identifier. attacker_number: Attacking driver's race number. target_number: Target driver's race number. current_lap: Current lap number in the race.

Returns: data: {laps_to_clear, viable, marginal}. meta.estimated: true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
current_lapYesCurrent lap number in the race.
session_keyYesOpenF1 session identifier.
target_numberYesTarget driver's race number.
attacker_numberYesAttacking driver's race number.

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

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful behavioral context by stating this is an estimate and exposing meta.estimated: true, indicating the result is a model calculation rather than an observed race fact. No contradiction with annotations.

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 short, front-loaded with the primary purpose, and closes with a compact Returns summary. However, the Args block duplicates information already present in the input schema, introducing mild redundancy.

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?

Given that an output schema exists and annotations cover safety/idempotence, the description supplies enough calling context: required parameters, expected outputs, and the estimated nature of the result. It does not explain viability thresholds, but that is not necessary for correct invocation.

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 Args section merely repeats the schema descriptions without adding constraints, units, edge-case guidance, or formatting rules. The schema carries the parameter documentation burden, 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?

The description opens with a specific verb and resource: 'Estimate whether an undercut is viable for the attacker against the target.' It also clarifies the return fields (laps_to_clear, viable, marginal), making the tool's scope unmistakable and distinguishable from broader pit/tyre strategy siblings.

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?

The purpose clearly implies when to use the tool, but the description does not explicitly state when to prefer it over related alternatives like f1_predict_pit_strategy or f1_tyre_degradation, nor does it mention any exclusions. Usage context is implied rather than stated.

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