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Hug0x0

mcp-reunion

reunion_get_road_daily_flow

Retrieve daily traffic-flow measurements for Réunion's national roads. Filters by station code or name and returns vehicle counts, lane, measurement type, date, and day classification.

Instructions

Daily traffic-flow measurements at fixed automatic counting stations on Réunion's national roads (RN). Each row is one station × one day × one channel/measurement type. Returns station name and code, channel (direction/lane), measurement nature (vehicles/heavy trucks), date, value, day type (weekday/weekend), school-holiday flag. Sorted by date descending. Combine with reunion_get_road_traffic for annualized averages.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stationNoStation code or name prefix match (e.g. "001", "RN1")
limitNoMax measurements to return (1-500, default 100)
Behavior4/5

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

With no annotations, the description carries full burden. It explains the data structure (one station × one day × one channel), fields returned, sorting (date descending), and combination hint. Though it omits explicit read-only or auth notes, the nature of 'measurements' implies non-destructive behavior.

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 tightly written in 3-4 sentences with front-loaded purpose. Every sentence adds value: what it returns, row structure, sorting, and cross-tool hint. No wasted words.

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 simplicity (2 parameters, no output schema), the description fully covers return fields, ordering, and usage hint. It provides sufficient context for an agent to understand and correctly invoke the tool without gaps.

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 both parameters are clearly described (station prefix match, limit with range/default). The description adds no extra context beyond what the schema already provides, so baseline 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 clearly states 'Daily traffic-flow measurements at fixed automatic counting stations on Réunion's national roads' – a specific verb-resource pair. It distinguishes itself from the sibling 'reunion_get_road_traffic' by noting that tool returns annualized averages, making the purpose distinct.

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

Usage Guidelines4/5

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

Explicitly advises to 'Combine with reunion_get_road_traffic for annualized averages,' providing clear context for when to use this tool vs. its sibling. While it doesn't formally list exclusions, the guidance is actionable and specific.

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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