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ourpr.

List trail systems

ourpr_list_trail_systems
Read-onlyIdempotent

List every trail system ourpr. maps. A trail system is a whole network of connected paths, measured end to end, not a single route through it.

Args:

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

Returns: { "count": number, "trail_systems": [{ "slug": string, // "trinity-trails" "name": string, "region": string | null, // "Fort Worth, TX" "total_mi": number, // the whole network, all branches "page_url": string }] }

Examples:

  • "What trail networks does ourpr. map?" -> no arguments

  • "How big is Trinity Trails?" -> read total_mi, or call ourpr_get_trail_system

  • Do not use when: you want one measured route. Use ourpr_list_courses with kind='trail'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
response_formatNoOutput format. 'markdown' reads well in a chat answer. 'json' carries every field for further computation.markdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYes
trail_systemsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, lowering the bar. The description adds value by clarifying the domain-specific meaning of 'trail system' and stating the return shape (count and array). It does not contradict annotations, though it repeats some schema details.

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 well-structured: a clear purpose sentence, then Args, Returns, Examples, and a 'Do not use when' note. It is front-loaded and each section earns its place, though the return schema partially duplicates the output schema.

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 tool with one optional parameter, an output schema, and strong readOnly/idempotent annotations, the description fully covers purpose, usage, disambiguation, and return semantics. Nothing an agent needs to call it 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 single parameter response_format is fully documented in the schema with enum and default. The description only restates the default and adds a practical note about markdown vs json, which is marginal. Baseline of 3 is appropriate.

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 opens with a precise verb ('List') and a scoped resource ('every trail system'), and immediately differentiates from single routes by defining a trail system as a network of connected paths. This distinguishes it from sibling ourpr_list_courses without ambiguity.

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 gives explicit usage examples and includes a 'Do not use when' clause pointing to ourpr_list_courses with kind='trail' for single measured routes. This clearly routes the agent to the correct sibling in the ambiguous case.

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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