f1verse MCP Server

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Who was where at the end of every lap.

f1_running_order

Read-onlyIdempotent

Retrieve lap-by-lap running order for a Formula 1 race to track position changes, lap churn, and biggest single gains, making it easy to analyze how the race unfolded.

Instructions

Who was where at the end of every lap. The order on track lap by lap, plus how much each lap churned and who made the biggest single gain. This is the data behind any position-change chart; use it for 'how did the race unfold' rather than fetching results and guessing.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "year": "<year>",
      "round": "<round>"
    },
    "name": "f1_running_order"
  }
}

Input Schema

Name	Required	Description	Default
`year`	Yes	Season year, e.g. 2026. Race data 2023+.
`round`	Yes	Round number within the season, 1-based.

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedv0.15.0Nov 9, 2026

addedInput schema / properties / round / minimum
```
Added value: +1
```
addedInput schema / properties / year / minimum
```
Added value: +1950
```

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": false,
+  "properties": {
+    "result": {
+      "type": "object"
+    }
+  },
+  "required": [
+    "result"
+  ],
+  "type": "object"
+}

First observedv0.1.0Nov 6, 2026

TDQS

A3.9/5.0

Behavior3/5

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

Annotations already declare this a safe, idempotent, read-only open-world query, so the safety bar is low. The description adds that the payload includes per-lap churn and biggest-gain figures, which is useful framing, but says nothing about coverage limits (the schema notes race data is 2023+ while year goes back to 1950) or response size for long races.

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?

Three sentences, front-loaded with the core definition and no filler. 'This is the data behind any position-change chart' is slightly promotional but still carries a routing signal, so nothing is genuinely wasted.

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?

An output schema exists, both parameters are fully documented, and annotations cover the safety profile, so the description only needs to establish scope and usage — which it does. The remaining gap is not distinguishing itself from the other race-narrative tools in the sibling set.

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 both parameters are plainly documented, including the 1-based round and the 2023+ data caveat. The description adds no parameter-level meaning beyond that, so the baseline 3 applies.

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?

States a specific resource and granularity — on-track order at the end of every lap — plus the derived extras (per-lap churn, biggest single gain). The final clause explicitly separates it from pulling session results and inferring order, which is exactly the confusion an agent could have with f1_session_results.

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?

Gives a clear use case ('how did the race unfold') and one explicit exclusion ('rather than fetching results and guessing'). It does not address the nearer overlap with siblings like f1_turning_points, f1_race_events or f1_battles, which also describe position change over a race, so the agent must still guess among those.

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