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sportiq-mcp

by io.github.Ninjabeam20

f1_head_to_head_pace

Read-onlyIdempotent

Compare lap-time pace distribution between two drivers in a session.

Args: session_key: OpenF1 session identifier. driver_a: First driver's race number. driver_b: Second driver's race number.

Returns: data: {driver_a_avg_s, driver_b_avg_s, delta_s, faster_driver}. meta.estimated: true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
driver_aYesFirst driver's race number.
driver_bYesSecond driver's race number.
session_keyYesOpenF1 session identifier.

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

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds meaningful context by stating the return shape and the behavioral trait 'meta.estimated: true', signaling that results are estimates rather than exact measurements. No annotation contradictions exist.

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 compact, front-loaded with the core purpose, and structured into Args/Returns sections. Every line contributes information, and there is no wasted text or redundant elaboration.

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

Completeness3/5

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

With an output schema present and annotations covering safety, the description is mostly sufficient. However, it says 'distribution' yet only returns averages (averages, delta, faster driver), which is a potential mismatch. It also omits any detail on lap selection methodology (e.g., whether pit laps are excluded) and gives no contextual guidance for choosing this over f1_race_pace_compare.

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 description's Args section merely repeats the same information as the schema fields. It adds no deeper meaning about parameter formats, constraints, or how session_key/driver numbers are resolved. A baseline of 3 is appropriate because the schema carries the semantic load.

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?

The description opens with a specific verb and resource: 'Compare lap-time pace distribution between two drivers in a session.' This clearly conveys the core operation. However, it does not explicitly differentiate from the closely named sibling f1_race_pace_compare, so the agent must infer the distinction from the 'head-to-head' framing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given about when to use this tool versus alternatives like f1_race_pace_compare or f1_get_lap_times. The description only states what the tool does and its parameters, leaving the selection criteria entirely to the agent's inference.

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