ThinAir Geo

trace_attributes

Read-onlyIdempotent

Trace attributes return road/network attributes for matched traces. Feed a GPS breadcrumb trail (2–100 points) and get per-segment road names, road classes, speed limits, surfaces, truck-route flags, and segment lengths along the matched route, plus a matcher confidence score. Sparsely-reported traces (consecutive fixes farther apart than the default breakage distance — e.g. fleet units reporting every several minutes on a highway) can split into multiple sections; increase breakage_distance_m to keep them as one. Total matched distance is limited to 200 km per call — split longer sparse traces by distance, not only by the 100-point cap. Use to audit what roads a vehicle actually used (speed-limit compliance, truck-route adherence, surface checks). For just the matched route line, use map_match.

Input Schema

TableJSON Schema

Name	Required	Description
`shape`	Yes	Raw 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.
`costing`	No	Transport mode the trace was recorded with (default: auto)
`search_radius`	No	Snap radius in meters around each point (1–100). Increase for noisy GPS.
`vehicle_profile`	No	Truck preset (DRY_VAN_53, FLATBED_48, BOX_TRUCK_26, …) — same 10-profile table as directions. Implies costing=truck.
`breakage_distance_m`	No	Maximum gap, in meters, between consecutive fixes before the trace is split into separate sections (100–20000). Increase for sparsely-reported GPS, such as fleet units reporting every several minutes on a highway. Optional — omitted uses the default.

Output Schema

TableJSON Schema

Name	Required	Description
`meta`	No
`edges`	Yes
`admins`	No
`costing`	Yes
`geojson`	No
`matched`	Yes
`geometry`	No
`confidence`	Yes	Matcher confidence 0–1
`edge_count`	Yes
`road_names`	Yes
`distance_km`	Yes
`costing_options`	No
`vehicle_profile`	No
`duration_seconds`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedSep 24, 2026

addedInput schema / properties / breakage_distance_m

Added value: +{
+  "description": "Maximum gap, in meters, between consecutive fixes before the trace is split into separate sections (100–20000). Increase for sparsely-reported GPS, such as fleet units reporting every several minutes on a highway. Optional — omitted uses the default.",
+  "maximum": 20000,
+  "minimum": 100,
+  "type": "integer"
+}

changedOutput schema / properties / meta / additionalProperties
```
Previous value: -{}New value: +true
```

addedOutput schema / properties / meta / properties

Added value: +{
+  "breakage_distance_m": {
+    "description": "Echoes the breakage_distance_m the caller requested, for confirmation — present only when the caller set it. This is the value SENT to the matcher, not a claim about what it did with it.",
+    "type": "integer"
+  },
+  "point_count": {
+    "type": "integer"
+  },
+  "upstream_ms": {
+    "type": "number"
+  }
+}

addedOutput schema / properties / meta / required

Added value: +[
+  "upstream_ms",
+  "point_count"
+]

AddedAug 17, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is covered. The description adds valuable behavioral context: sparse traces can split into multiple sections, and total matched distance is capped at 200 km. This goes beyond the structured hints, though it stops short of describing output schema details (which are separately provided).

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 compact yet dense: three sentences carry purpose, edge-case handling, and alternatives. The key purpose is front-loaded, and the usage note is placed at the end. It earns its length, though the split/limit guidance could be tightened slightly.

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 output schema exists (and the description enumerates returned attributes), the description covers all operational needs: input range (2–100 points), spatial limits, splitting behavior, and use-case fit. It leaves no critical gap for an agent to decide when and how to call it.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds meaning for breakage_distance_m (explains why to increase it for sparse GPS) and vehicle_profile (implies costing=truck), and clarifies the shape parameter's optional time field. These enrich the schema without redundancy.

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 clear, specific verb–resource pairing ('return road/network attributes for matched traces') and enumerates the exact attributes returned (road names, classes, speed limits, surfaces, truck-route flags, segment lengths, confidence score). It explicitly distinguishes itself from the sibling map_match ('For just the matched route line, use map_match'), so an agent can differentiate without opening schemas.

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 states explicit use cases ('audit what roads a vehicle actually used – speed-limit compliance, truck-route adherence, surface checks') and explicitly names the alternative for route geometry. It also gives operational guidance on when to adjust breakage_distance_m and how to handle the 200 km limit, which is actionable context.

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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