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formula1_json_results

Fetch a Formula 1 session dataset as raw JSON for a meeting, returning result rows, classifications, gaps, lap counts, points, qualifying times, and meeting metadata.

Instructions

One Formula 1 session dataset's rich JSON payload. Returns one session dataset's full JSON payload for a meeting, exactly as formula1.com's own API returns it: session metadata, the result rows with every field the API sends, classification codes, gaps, lap counts, points, qualifying segment times and the meeting block. The dataset is the closed set from formula1-json-meeting-datasets; practice requires its numbered session and every other dataset rejects one. The payload is kept verbatim in data, so no rich field is reshaped or dropped.

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": {
      "meeting": "<meeting>",
      "dataset": "<dataset>"
    },
    "name": "formula1_json_results"
  }
}

Input Schema

Name	Required	Description
`dataset`	Yes	Dataset: race, qualifying, starting-grid, pitstops, fastestlaps, sprint, sprint-grid, sprint-shootout or practice
`meeting`	Yes	Numeric meeting id from formula1-json-meetings example(1299)
`session`	No	Practice session, 1 to 3; required for the practice dataset and rejected for the others example(1)

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.17.20Nov 12, 2026

TDQS

A4.1/5.0

Behavior3/5

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

No annotations are supplied, so the description carries the full behavioral burden. It does disclose useful traits: the payload is verbatim as formula1.com's API returns it, nothing is reshaped or dropped, and it lives under 'data'. It says nothing about auth needs, rate limits, response size, or error behavior for invalid dataset/session combinations beyond the session rejection rule, so a 3 fit given zero annotation coverage.

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?

Front-loaded with the core statement and three sentences total. The opening sentence ('One Formula 1 session dataset's rich JSON payload') is nearly redundant with the second sentence that restates the same return, costing a bit of tightness, but nothing else is 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?

No output schema exists, so the description must describe the return — and it does, listing payload components and the 'data' container. For a three-parameter read tool this is close to complete; the gaps are failure modes and payload size, which are minor relative to what is covered.

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 coverage is 100%, so the baseline is 3. The description adds the provenance constraint on dataset (closed set from formula1-json-meeting-datasets) and the 'kept verbatim in data' container detail. The practice/session rule is largely a restatement of what the schema already documents, so it does not add much beyond the schema.

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 verb and resource (returns one session dataset's full JSON payload for a meeting) and enumerates the payload contents: session metadata, result rows, classification codes, gaps, lap counts, points, qualifying segment times and the meeting block. It also anchors itself to formula1-json-meeting-datasets as the source of the closed dataset set, which helps separate it from sibling F1 tools.

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?

Implies the workflow: the dataset set is closed and comes from formula1-json-meeting-datasets, so an agent knows to resolve datasets there first. It also states the session-number rule (required for practice, rejected for every other dataset). It does not explicitly contrast against the curated formula1_session_results sibling, so the when-to-prefer-this-tool routing is only implied.

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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