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map_match

Read-onlyIdempotent

Map matching snaps raw GPS breadcrumbs to the road network. Feed a recorded trace (2–100 points) and get back the road-accurate route that was actually driven: matched geometry, distance, time, road names, and a 0–1 match confidence. Use for fleet trace cleanup, mileage from GPS logs, and reconstructing routes from telemetry. road_names come from the route's maneuver points, so they cover the roads the route turns onto or starts on — for the name of every segment actually travelled, use trace_attributes. For planning a route between places, use directions instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
shapeYesRaw GPS breadcrumb trail in travel order — 2 to 100 points of { lat, lon, time? }. `time` is optional epoch seconds; timestamps improve match quality on noisy traces.
costingNoTransport mode the trace was recorded with (default: auto)
search_radiusNoSnap radius in meters around each point (1–100). Increase for noisy GPS.
vehicle_profileNoTruck preset (DRY_VAN_53, FLATBED_48, BOX_TRUCK_26, …) — same 10-profile table as directions. Implies costing=truck.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
legsYes
metaNo
costingYes
geojsonNo
matchedYes
geometryNo
confidenceYes0–1: fraction of input GPS points within the snap tolerance of the matched route
road_namesYes
distance_kmYes
costing_optionsNo
vehicle_profileNo
duration_secondsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, covering safety. The description adds meaningful behavioral context beyond the schema: it discloses the return contents (matched geometry, distance, time, road names, confidence) and exposes a limitation (road_names derived only from maneuver points, not every segment). This is useful, non-obvious 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?

Four sentences, each with a distinct job: the core operation, use cases, a caveat about road names, and a routing to the correct sibling. The most important information is front-loaded, and there is no filler or repetition of schema details.

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 annotations, a fully described schema, and an output schema, the description covers use cases, limitations, alternatives, and expected results. An agent has everything it needs to decide when to call this tool and what to expect from it.

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 description coverage is 100%, so the schema fully documents parameters such as shape, costing, search_radius, and vehicle_profile. The description reinforces the 2–100 point range and mentions the optional `time` field indirectly, but does not add meaning beyond the schema. 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 opens with a specific verb and resource: 'snaps raw GPS breadcrumbs to the road network' and explicitly lists the returned artifacts (matched geometry, distance, time, road names, confidence). It distinguishes itself from related tools by naming `directions` for route planning and `trace_attributes` for per-segment road names.

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 gives concrete use cases ('fleet trace cleanup, mileage from GPS logs, reconstructing routes from telemetry') and explicitly states when not to use it: 'For planning a route between places, use `directions` instead.' It also routes the agent to `trace_attributes` for a different need, leaving no ambiguity.

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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