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Crawlora MCP

by net.crawlora
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formula1_team

Read-only

Get one Formula 1 team profile and return the team's name, current drivers and season, career and team statistics. team_slug comes from formula1_team_standings or a season page, for example mercedes; an unknown slug is a 404. Use formula1_team_standings for the constructors' table instead.

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": {
      "team_slug": "<team_slug>"
    },
    "name": "formula1_team"
  }
}

Input Schema

NameRequiredDescriptionDefault
team_slugYesRequired team profile slug from formula1-team-standings or a season page, such as mercedes.

Output Schema

NameRequiredDescriptionDefault
dataYesThe tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered; the description adds useful behavior beyond that — the field set returned and the 404 failure mode for an unknown slug. It does not discuss rate limits or caching, but for a read-only lookup tool that is a minor gap.

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?

Three tight sentences, front-loading what is returned before identifier provenance and the sibling redirect. No sentence is filler, and the alternative tool is mentioned last where an agent checking for the right tool will still see it.

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?

With an output schema available and annotations covering read-only/open-world behavior, the description only needs to convey scope, identifier provenance, and sibling routing — all present. Nothing required to invoke the tool correctly is missing.

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 coverage is 100% and the schema's own parameter text already says the slug comes from standings or a season page and gives the 'mercedes' example, so the description largely repeats structured data. The only added meaning is the 404 consequence for a bad slug, which is modest beyond baseline.

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 states a specific verb and resource ('Get one Formula 1 team profile') and enumerates the returned content: name, current drivers, season, career and team statistics. It explicitly contrasts itself with formula1_team_standings, so the agent can tell the two apart without inspecting schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives the positive case (fetch a single team profile), names a valid source for the identifier ('formula1_team_standings or a season page'), and explicitly redirects the standings use case to formula1_team_standings. An unknown-slug 404 is also disclosed, which is genuine when-to-use context.

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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