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Lavoiedavidw

Charlotte-Mecklenburg MCP Server

by Lavoiedavidw

get_trash_schedule

Get garbage, recycling, and yard waste collection days for any Charlotte address. Enter an address or coordinates to retrieve pickup schedule, route, provider, and recycling week.

Instructions

Look up garbage, recycling, and yard waste collection schedule.

Provide either address (free-form string) or both lat and lon. Queries the City of Charlotte Solid Waste Collection polygon layer via point-in-polygon and returns per-stream day, provider, route, and (for recycling) the GREEN/ORANGE alternating week parsed from the route note.

Args: address: Free-form address, e.g. "2437 Remount Rd, Charlotte, NC". lat: Latitude, used with lon if address is not given. lon: Longitude, used with lat if address is not given.

Returns: Dict with garbage, recycling, yard_waste sub-dicts (each with day, provider, route, route_note, and for recycling recycling_week), plus lat, lon, resolved_address, and a human-readable summary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNo
lonNo
addressNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries the full transparency burden. It explains the underlying method (point-in-polygon query against the City of Charlotte Solid Waste polygon layer) and the output structure (per-stream day, provider, route, recycling week). It does not mention error handling or edge cases, but this is a solid disclosure.

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 well-structured with Args and Returns sections. Every sentence adds value: describing the query method, parameter options, and return fields. It is appropriately sized for the tool's complexity without extraneous information.

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 tool has an output schema and the description already explains the return dict, parameters, and data source, the description is complete for an agent to select and invoke the tool correctly. No significant gaps are present.

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

Parameters5/5

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

The schema has 0% description coverage, but the description fully compensates. Each parameter (address, lat, lon) is clearly defined, including examples and the relationship between them (address vs lat/lon). This is exactly what parameter semantics should provide.

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 specific verb and resource: 'Look up garbage, recycling, and yard waste collection schedule.' It clearly distinguishes this from sibling tools (crime, zoning, projects, etc.) and even specifies the City of Charlotte context in the second sentence.

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?

The description provides explicit usage instructions: 'Provide either address or both lat and lon,' with details on how each parameter is used. It does not explicitly name alternative tools, but given the unique focus on trash schedules, the usage context is clear.

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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