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sportiq-mcp

by io.github.Ninjabeam20

f1_get_lap_times

Read-onlyIdempotent

Return lap times for a driver in a specific F1 session.

Args: session_key: OpenF1 session identifier. driver_number: Driver's race number (e.g. 1 for Verstappen). limit: Max laps to return, 1..200 (default 100 — covers most full races). offset: Number of laps to skip for paging (default 0).

Returns: data.laps: page of lap objects with lap_number and lap_duration. OpenF1 does not put compound/tyre_life here — those live on the stints endpoint. data.pagination: {total, count, offset, limit, has_more, next_offset}. meta.source: adapter that served the data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax laps to return, 1..200 (default 100 — covers most full races).
offsetNoNumber of laps to skip for paging (default 0).
session_keyYesOpenF1 session identifier.
driver_numberYesDriver's race number (e.g. 1 for Verstappen).

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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds valuable operational behavior: pagination details (has_more, next_offset), response shape, and the data-availability caveat about compound/tyre_life living elsewhere. 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.

Conciseness5/5

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

Well-structured with a one-line purpose, an Args section, and a Returns breakdown. Every sentence earns its place; the description is detailed yet concise, with no redundant repetition of schema descriptions.

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?

For a read-only, paged list endpoint with an output schema, this description is complete: it covers required parameters, defaults, response structure, pagination semantics, source metadata, and a data-availability caveat. Nothing an agent needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3, but the description adds practical semantics beyond the schema: a concrete driver number example ('1 for Verstappen'), why the default limit of 100 covers most races, and how offset enables paging. This enriches parameter understanding.

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 a specific verb and resource: 'Return lap times for a driver in a specific F1 session.' This clearly distinguishes it from siblings like f1_get_race_results, f1_get_standings, and f1_get_drivers, leaving no ambiguity about what the tool does.

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?

Provides clear context on paging and defaults, and explicitly notes that compound/tyre_life are not included and live on the stints endpoint, which helps prevent misuse when tyre data is needed. It does not explicitly name sibling tools as alternatives, but the guidance is sufficient for correct selection.

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