FirstCycling MCP Server

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 describes what the tool returns (formatted string with results, positions, categories, chronological organization) and implies it's a read-only operation, but lacks details on error handling, rate limits, authentication needs, or data freshness. It adds some value but is incomplete for a tool with no 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded with the core purpose, followed by usage notes, examples, and return details. Every sentence adds value without redundancy, and the length is appropriate for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, 0% schema coverage, and no output schema, the description does a good job explaining purpose, parameters, and returns. However, it lacks details on error cases (e.g., invalid IDs/years), performance limits, or data sources, leaving some gaps for a tool with rich output.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It explains that 'rider_id' corresponds to a professional cyclist's identifier (with examples like '16973' for Tadej Pogačar) and 'year' is a calendar year for results (e.g., 2023). This adds meaningful context beyond the bare schema, though it doesn't detail format constraints or validation rules.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('retrieve detailed results'), resource ('professional cyclist'), and scope ('for a specific year'). It distinguishes from sibling tools by focusing on comprehensive yearly performance data rather than specific race types (e.g., 'get_rider_grand_tour_results') or general rider information ('get_rider_info').

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

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

The description explicitly provides when-to-use guidance by stating 'If you don't know the rider's ID, use the search_rider tool first to find it by name,' naming an alternative tool. It also implies usage context through example scenarios (e.g., 'Get 2023 results for Tadej Pogačar'), though it doesn't explicitly exclude other use cases.

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