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ourpr.

by io.github.joeyaflores
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List published running courses

ourpr_list_courses
Read-onlyIdempotent

List every running course published on ourpr., with its place, distance, climb and next race date.

Each course is a MEASURED centerline built from recorded runs, with elevation from the USGS 3DEP 10 m model. It is not a course map traced by hand.

START HERE. Every other course tool takes a slug, and this is where slugs come from.

Args:

  • query (string, optional): Match the name and the place. Partial, case-insensitive.

  • kind ('race' | 'trail' | 'route', optional): Keep one kind.

  • course_type ('loop' | 'out-and-back' | 'point-to-point', optional): Keep one shape.

  • min_distance_mi / max_distance_mi (number, optional): Bound the distance in miles.

  • upcoming_only (boolean): Keep only courses whose race date has not passed. Default false.

  • limit (number): 1 to 50. Default 20.

  • offset (number): Skip this many. Default 0.

  • response_format ('markdown' | 'json'): Default 'markdown'.

Returns: { "total": number, // matched the filters "count": number, // in this answer "offset": number, "has_more": boolean, "next_offset": number, // present when has_more "published_total": number, // every published course "courses": [{ "slug": string, // "boston-marathon" "name": string, // the event's own name, title sponsor included "place": string | null, // "Hopkinton → Boston, MA" "kind": string, "course_type": string, "distance_mi": number, "climb_ft": number | null, "steepest_pct": number | null, "next_race_date": string | null, // YYYY-MM-DD "days_out": number | null, // null once the date has passed "page_url": string }] }

The order is the race calendar: the soonest race first, then undated courses by distance.

Examples:

  • "What marathons does ourpr. have?" -> min_distance_mi=26

  • "Which race is next?" -> upcoming_only=true, limit=1

  • "Anything in Fort Worth?" -> query="fort worth"

  • Do not use when: you want the hills on one course. Use ourpr_get_course_terrain.

Error handling:

  • Returns "No course matched" with the filters echoed when nothing matches.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNoKeep only this kind. 'race' is a published event course, 'trail' is a trail segment, 'route' is an everyday loop.
limitNoThe most courses to return.
queryNoMatch against the course name and its place. Case-insensitive, partial. Example: 'boston', 'fort worth', 'half'.
offsetNoHow many courses to skip. Use it with `next_offset`.
course_typeNoKeep only this shape of course.
upcoming_onlyNoKeep only courses whose next race date is today or later. A course with no date is dropped by this filter.
max_distance_miNoKeep only courses no longer than this, in miles.
min_distance_miNoKeep only courses at least this long, in miles.
response_formatNoOutput format. 'markdown' reads well in a chat answer. 'json' carries every field for further computation.markdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYesCourses in this answer.
totalYesCourses that matched the filters.
offsetYes
coursesYes
has_moreYes
next_offsetNo
published_totalYesEvery published course, before any filter.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the annotations already signaling a safe read operation, the description discloses meaningful behavioral traits: courses are measured centerlines built from recorded runs, elevation comes from the USGS 3DEP 10 m model, courses are sorted by soonest race date, and 'days_out' becomes null once the date passes. It also exposes the 'No course matched' error behavior.

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?

The description is front-loaded with the core purpose and the most important workflow guidance, and it is well organized into Args, Returns, Examples, and Error handling. However, the Args and Returns blocks partially duplicate the input schema and output schema that are already provided as structured fields, which adds some redundancy and prevents a perfect score.

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?

For a 9-parameter tool with no required parameters, the description covers entry-point usage, parameter semantics, default values, result ordering, error handling, and when to route to a sibling tool. Nothing an agent needs to correctly select and invoke this tool appears to be missing.

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 description coverage is 100%, so the baseline is 3, but the description adds value through natural-language usage examples ('What marathons does ourpr. have?' -> min_distance_mi=26) and by restating defaults in a compact, readable form. It mostly repeats the schema, but the examples and 'upcoming_only' semantics improve parameter understanding beyond the raw 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?

The first sentence names a specific verb ('List'), a specific resource ('every running course published on ourpr.'), and the core returned attributes: place, distance, climb, and next race date. It also clarifies that this tool is the source of slugs for every other course tool, distinguishing it from siblings like ourpr_get_course and ourpr_get_course_terrain.

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 explicitly says 'START HERE' and explains that every other course tool takes a slug, which is where slugs come from. It also gives a clear when-not-to-use rule: 'when you want the hills on one course. Use ourpr_get_course_terrain.' The worked examples map natural language requests to concrete parameter values.

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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