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OpenF1 MCP Server

OpenF1 MCP Server

by ragulhari

Intervals

get_intervals
Read-onlyIdempotent

Retrieve gap-to-car-ahead and gap-to-leader intervals during a race, then filter by driver or time to reduce large results.

Instructions

Gap to the car ahead and gap to the leader during a race (about every 4 s). Large data: filter by driver and time.

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum number of records to return (default 200, max 1000). A summary is added when the result is larger.
fieldsNoReturn only these attributes. Use this to make large results smaller.
date_toNoEnd of the time range (UTC, ISO 8601). Filters on "date" <= value.
filtersNoExtra comparison filters, e.g. [{"field":"lap_duration","op":"<","value":90}]. Allowed fields: date, driver_number, gap_to_leader, interval, meeting_key, session_key. Array fields cannot be filtered.
date_fromNoStart of the time range (UTC, ISO 8601), e.g. "2025-11-09T17:05:00Z". Filters on "date" >= value.
meeting_keyNoMeeting (Grand Prix weekend) identifier. Use "latest" for the current or most recent meeting.
session_keyNoSession identifier. Use "latest" for the current or most recent session.
driver_numberNoDriver number(s) for the season, e.g. 1, 44, 81. Give a list to get more than one driver.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds genuinely new context—sampling interval of roughly 4 seconds and the warning that the dataset is large—but says nothing about default result size behavior, summaries, or ordering.

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?

Two short sentences with no filler, and the high-value information (what the data is, how often it is sampled) is front-loaded. The telegraphic "Large data: filter by driver and time" phrasing is slightly clipped but still earns its place as an efficiency warning.

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 8 optional parameters and no output schema, the description could do more: it never names the returned attributes (interval, gap_to_leader), never states that a session or meeting scope is effectively needed, and never explains the summary behavior that the limit parameter references. The fully documented schema compensates partially, making this adequate but with clear gaps.

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%, so every parameter (limit, fields, date_from/date_to, filters, meeting_key, session_key, driver_number) is already documented in the schema. The description's "filter by driver and time" only echoes driver_number and the date range, adding no syntax or semantics beyond the schema. Baseline 3 applies.

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?

States the specific resource (gap to car ahead and gap to leader) plus the sampling cadence (~every 4 s) and the context (during a race), so an agent knows exactly what data comes back. It does not explicitly contrast with siblings like get_position or get_laps, which is the only thing keeping it from a 5.

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?

"Large data: filter by driver and time" implies how to call it efficiently, but there is no explicit when-to-use / when-not-to-use statement and no named alternative (e.g. get_position for raw classification). Usage is implied rather than spelled out.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.