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r-huijts

FirstCycling MCP Server

by r-huijts

get_rider_one_day_races

Retrieve detailed one-day race results for a specific cyclist, including positions, times, and categories. Filter results by year for targeted insights. Requires the rider's ID for accurate data access.

Instructions

Get a rider's results in one-day races, optionally filtered by year. This tool retrieves detailed information about a rider's performance in one-day races (classics and one-day events). It provides comprehensive data about positions, times, and race categories. Results can be filtered by a specific year.

Note: If you don't know the rider's ID, use the search_rider tool first to find it by name.

Example usage:
- Get one-day race results for Mathieu van der Poel (ID: 16672)
- Get 2023 one-day race results for Wout van Aert (ID: 16948)

Returns a formatted string with:
- Results in one-day races organized by year
- Position and time for each race
- Race category and details
- Chronological organization

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rider_idYes
yearNo

Implementation Reference

  • The core logic for retrieving a rider's one-day races results using the Rider class method. This is likely the implementation used by the MCP tool 'get_rider_one_day_races'.
    def one_day_races(self):
    	"""
    	Get the rider's results at major one-day races.
    
    	Returns
    	-------
    	RiderEndpoint
    	"""
    	return self._get_endpoint(stats=1, k=2)
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adequately describes the tool's function and output format (a formatted string with organized results), but lacks details on potential limitations, error handling, or performance aspects like rate limits. It doesn't contradict annotations, but could be more comprehensive for a tool with no structured behavioral hints.

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 well-structured and front-loaded, starting with the core purpose, followed by details, usage notes, examples, and return format. Every sentence adds value without redundancy, and it's appropriately sized for the tool's complexity, making it easy to scan and understand quickly.

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?

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is largely complete. It covers purpose, usage, parameters, and output format adequately. However, it could improve by mentioning any constraints on the 'year' parameter or error cases, but overall it provides sufficient context for an agent to use the tool effectively.

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?

The schema description coverage is 0%, so the description must compensate. It effectively explains the semantics of both parameters: 'rider_id' is required for identifying the rider, with a note to use 'search_rider' if unknown, and 'year' is optional for filtering results. This adds meaningful context beyond the bare schema, though it doesn't specify format constraints or examples for the parameters.

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 the tool's purpose with specific verbs ('get', 'retrieves') and resources ('rider's results in one-day races'), distinguishing it from siblings like 'get_rider_grand_tour_results' or 'get_rider_stage_races' by focusing exclusively on one-day races. It explicitly mentions the type of races (classics and one-day events) and the data provided (positions, times, race categories).

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?

The description provides explicit guidance on when to use this tool vs. alternatives, including a prerequisite note to use 'search_rider' if the rider ID is unknown, and it distinguishes usage from other tools by specifying it's for one-day races only. The example usage further clarifies typical scenarios, making it easy for an agent to determine applicability.

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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