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toruproject

MapNetwork MCP Server

by toruproject

compute_route

Compute a walking or driving route between two locations, returning coordinates and resolved endpoints for overlay on a map image.

Instructions

Compute a walking or driving route between two locations.

Returns the route as an ordered list of coordinates, plus the resolved from/to locations. Pass the result directly to generate_map's route parameter to overlay it on a map image. Use from/to as markers in generate_map to place pins at the start and end points.

Typical flow:

  1. route = compute_route(from_location={"label": "A"}, to_location={"label": "B"})

  2. generate_map(route=route, markers=[route["from"], route["to"]], ...)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
from_locationYes
to_locationYes
modeNowalking
Behavior3/5

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

Without annotations, the description discloses the return value structure (ordered list of coordinates, resolved from/to locations) and the default value for mode. However, it does not cover edge cases, error handling, or restrictions, leaving some behavioral aspects undisclosed.

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 concise and well-structured, starting with the core purpose, then explaining the output and usage pattern. It avoids unnecessary repetition and provides a clear typical flow. Slightly verbose for a simple tool, but effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (3 parameters, no output schema, no annotations), the description provides enough information to use it successfully. It covers the main use case and integration with generate_map, though it could mention error scenarios for completeness.

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?

The schema has no descriptions for parameters, so the description must compensate. It explains the mode parameter (walking/driving) and provides an example for location objects using 'label'. But it does not fully specify the expected structure of location objects, relying on the example. This adds some meaning but not comprehensive detail.

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 that the tool computes a route between two locations for walking or driving. It distinguishes itself from siblings by linking its output to generate_map, making the tool's specific role in the workflow clear.

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 a typical flow showing how to use compute_route in conjunction with generate_map. It explains the purpose and how to pass the result. While it doesn't explicitly state when not to use this tool, the sibling tools and context make the usage scope 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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