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Get race plan requirements

get_race_plan_requirements

Use this before create_race_plan when the user gives only a race name or incomplete inputs. Returns the required course and runner inputs and the next steps an assistant should take, including catalog matches when available.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
race_nameNoOptional race name the user mentioned, e.g. Val d'Aran PDA.
known_course_urlNoOptional candidate direct GPX/FIT/CRSProf URL if already found.
has_runner_profileNoWhether the user already provided a USRProf runner profile or runner evidence.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
workflowYesAlways race_plan_requirements.
race_nameNoRace name supplied by the user, if any.
next_stepsNo
catalog_matchesNo
required_inputsNo
course_acquisition_fallbackNo

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the transparency burden. It discloses that the tool returns required inputs and next steps, and conditionally includes catalog matches when available. It does not explicitly state it's read-only, but the 'get' prefix and context imply a non-destructive query, and the returned data is well described.

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?

Two sentences that immediately front-load the usage context, then state exactly what the tool returns. No filler, every word earns its place.

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?

Given the simple nature (3 optional params) and that an output schema exists, the description is sufficiently complete. It explains when to use, what it returns, and the relationship to create_race_plan, so the agent can select and invoke it correctly without additional context.

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%, with all three optional parameters documented in the schema. The tool description adds overall usage context but does not provide additional semantics for individual parameters beyond what the schema already offers, so 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?

The description clearly states the tool's function with a specific verb 'Returns' and resource 'required course and runner inputs' plus 'next steps'. It distinguishes itself from siblings by explicitly referencing create_race_plan and mentioning catalog matches.

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?

Provides explicit when-to-use guidance: 'Use this before create_race_plan when the user gives only a race name or incomplete inputs.' This clearly frames the tool as a prerequisite/helper, differentiating it from the create_race_plan sibling.

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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TDQS

A4.1/5.0
Disambiguation4/5

Each tool targets a distinct step in the course/race-planning workflow, and descriptions carefully separate concerns like importing, enriching, segmenting, and generating a plan. The main ambiguity is among the multiple get_*_requirements helpers, but their target phases are clear enough to avoid persistent misselection.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (create_, enrich_, export_, generate_, get_, import_, search_, submit_, validate_). There is no mixing of camelCase or inconsistent verb styles, making the API predictable and easy to navigate.

Tool Count4/5

With 16 tools, the server is at the upper edge of the ideal range but each tool serves a distinct purpose in a complex pipeline covering course import, enrichment, segmentation, runner profiling, plan creation, PDF export, validation, and catalog submission. The count feels justified for the domain rather than bloated.

Completeness4/5

The toolset covers the full lifecycle from course discovery/import through enrichment, segmentation, runner profiling, race plan generation, PDF export, and catalog submission. Minor gaps exist, such as lack of artifact list/delete/update tools and no direct race-plan editing, but agents can work around these via get_artifact and get_job.

Resources