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Glama

Openf1

by io.github.pipeworx-io

Get Laps

get_laps
Read-onlyIdempotent

Lap times / telemetry for an F1 session: per-lap duration, sector times, speed-trap, pit-out flag. Live race data. session_key="latest" for the most recent / ongoing session. Optionally filter by driver_number or lap_number.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lap_numberNoRestrict to a single lap.
session_keyYesSession key, or 'latest' for the most recent session. Required.
driver_numberNoRestrict to a single driver, e.g. 1, 44.

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "session_key": "latest"
+  },
+  {
+    "driver_number": 1,
+    "lap_number": 5,
+    "session_key": 9876
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, but the description adds value by detailing the returned fields (sector times, speed-trap, pit-out flag) and labeling it as 'Live race data'. No contradictions 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?

The description is extremely concise: two sentences that front-load the purpose and key details. Every word adds value, with no redundant or ambiguous phrasing.

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?

For a simple data retrieval tool, the description covers the main output fields and usage hints. It lacks details on pagination or rate limits, but given the low complexity and lack of output schema, this is sufficient.

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%, with each parameter having a description. The description merely repeats that filtering by driver_number or lap_number is optional, adding no extra meaning beyond the schema. Baseline 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 clearly states it provides 'Lap times / telemetry for an F1 session' with specific data fields (per-lap duration, sector times, speed-trap, pit-out flag). It distinguishes from sibling tools like get_drivers and get_sessions by focusing on lap-level data.

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 includes guidance on using 'session_key=latest' for live data and optional filtering by driver_number or lap_number. However, it lacks explicit when-not-to-use or direct alternatives, but for a data retrieval tool, this is adequate.

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

A3.5/5.0
Disambiguation2/5

Several tool clusters have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions across the same 5,756-tool catalog, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The polymarket_* family also overlaps heavily (edges, arbitrage, fill_risk, kalshi_spread all surface tradeable discrepancies), and entity_profile/compare_entities/recent_changes all fan out to overlapping SEC/news/patent sources. Descriptions are detailed, but the set contains intentional near-duplicates that make selection genuinely ambiguous.

Naming Consistency2/5

Naming follows no single convention: get_drivers/get_laps/get_meetings/get_sessions use verb_noun, polymarket_arbitrage/polymarket_edges are noun-phrase domain prefixes, pipeworx_feedback/pipeworx_trending use a vendor prefix, remember/recall/forget are bare verbs, and ask_pipeworx variants mix with action phrases like discover_tools, deep_research, and validate_claim. The inconsistency makes it harder to predict related tool names.

Tool Count2/5

35 tools is heavy, and the count is severely mismatched to the server name 'Openf1': only 4 of 35 tools (get_drivers, get_laps, get_meetings, get_sessions) are actually F1-related, while the other 31 are a general-purpose Pipeworx data/prediction-market platform. The number itself could be reasonable for a broad data hub, but for an F1 server it is bloated with unrelated functionality.

Completeness2/5

For the stated F1 domain, the surface is severely incomplete: it covers meetings, sessions, drivers, and laps but lacks race results, qualifying results, standings, pit stops, or constructor data — major gaps for any F1 use case. The Pipeworx side is far more complete (query, research, entities, verification, memory, subscriptions), but that completeness doesn't serve the server's apparent purpose.

Resources