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by ai.mapmap
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match_trace

Snap a recorded GPS trace to the road network and say what it actually travelled over. Provide shape (2 to 2000 recorded points, oldest first) or encoded_polyline (the same trace as a polyline6 string) (not both) plus the costing it was travelled under: "auto" (default), "truck", "bicycle", "pedestrian" or "motor_scooter". Costing decides which roads the trace may match onto, so a walk matched as "auto" snaps to the carriageway rather than the footpath. Returns the matched path as geometry_polyline6 (the snapped roads, not your raw points) with its distance_m and duration_s, then the roll-ups: by_road_class and by_admin (distance and time, longest first), by_surface (distance), and toll, bridge and tunnel totals. This is how you turn a dashcam or telematics log into a report, which country and region the driving happened in, how much of it was motorway, how much was tolled, how much was unpaved. Honesty: the roll-ups are summed per matched road segment, so they need not add up to distance_m exactly, and segments the map records no surface or admin area for are left out of that breakdown rather than filed under a guess, an entry in by_admin with null codes is exactly that, counted and not attributed. Needs the MapMap gateway (GATEWAY_URL + GATEWAY_API_KEY); there is no direct-backend fallback.

Input Schema

NameRequiredDescriptionDefault
shapeNoThe trace as an ordered list of recorded points, oldest first. Between 2 and 2000 points. Provide this or `encoded_polyline`.
costingNoCosting model the trace was travelled under: `auto` (default), `truck`, `bicycle`, `pedestrian` or `motor_scooter`. It decides which roads the trace may be matched onto, so a walked trace matched as `auto` snaps to the carriageway rather than the path.
encoded_polylineNoThe trace as a Google encoded polyline with six digits of decimal precision (polyline6), the geometry `route` and `match_trace` themselves return. Provide this or `shape`.

Output Schema

NameRequiredDescriptionDefault
tollYesTolled portion of the matched path.
bridgeYesBridge portion of the matched path.
tunnelYesTunnel portion of the matched path.
costingYesCosting the trace was matched under.
summaryYesOne-line human summary of the match, for reading aloud.
by_adminYesDistance and time by administrative area, longest first. An entry whose codes are all null covers segments the graph could not place in any admin area, counted honestly rather than guessed at.
by_surfaceYesDistance by road surface (`paved`, `paved_smooth`, `gravel`, …), longest first. Empty when the graph records no surface for any matched segment.
distance_mYesLength of the matched path in metres.
duration_sYesTravel time along the matched path in seconds, from the engine's own time model.
edge_countYesHow many road segments the trace matched onto.
by_road_classYesDistance and time by road class (`motorway`, `primary`, `residential`, …), longest first.
geometry_polyline6YesThe matched path as a Google encoded polyline with six digits of decimal precision (polyline6). This is the trace snapped to real roads, not the raw input.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / encoded_polyline / description
      Previous value: -"The trace as a Google encoded polyline with six digits of decimal\nprecision (polyline6) — the geometry `route` and `match_trace`\nthemselves return. Provide this or `shape`."New value: +"The trace as a Google encoded polyline with six digits of decimal\nprecision (polyline6), the geometry `route` and `match_trace`\nthemselves return. Provide this or `shape`."
    • changedOutput schema / properties / by_admin / description
      Previous value: -"Distance and time by administrative area, longest first. An entry\nwhose codes are all null covers segments the graph could not place\nin any admin area — counted honestly rather than guessed at."New value: +"Distance and time by administrative area, longest first. An entry\nwhose codes are all null covers segments the graph could not place\nin any admin area, counted honestly rather than guessed at."
  2. Changed5 schema fields changed
    • addedInput schema / $defs / LatLon / anyOf
      Added value: +[
+  {
+    "properties": {
+      "lat": {
+        "description": "Latitude in decimal degrees (−90 to 90).",
+        "format": "double",
+        "type": "number"
+      },
+      "lon": {
+        "description": "Longitude in decimal degrees (−180 to 180).",
+        "format": "double",
+        "type": "number"
+      }
+    },
+    "required": [
+      "lat",
+      "lon"
+    ],
+    "type": "object"
+  },
+  {
+    "description": "GeoJSON position [lon, lat]: longitude FIRST.",
+    "items": {
+      "format": "double",
+      "type": "number"
+    },
+    "maxItems": 2,
+    "minItems": 2,
+    "type": "array"
+  }
+]
    • changedInput schema / $defs / LatLon / description
      Previous value: -"A WGS84 coordinate pair in decimal degrees."New value: +"A WGS84 coordinate in decimal degrees: a {lat, lon} object (preferred), or a GeoJSON [lon, lat] array with LONGITUDE FIRST, the same order as every polygon field on this server."
    • removedInput schema / $defs / LatLon / properties
      Removed value: -{
-  "lat": {
-    "description": "Latitude in decimal degrees (−90 to 90).",
-    "format": "double",
-    "type": "number"
-  },
-  "lon": {
-    "description": "Longitude in decimal degrees (−180 to 180).",
-    "format": "double",
-    "type": "number"
-  }
-}
    • removedInput schema / $defs / LatLon / required
      Removed value: -[
-  "lat",
-  "lon"
-]
    • removedInput schema / $defs / LatLon / type
      Removed value: -"object"
  3. Added

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so well: it discloses the exact input alternatives and mutual exclusion, the return shape, the honesty caveat that roll-ups may not sum to `distance_m`, how unattributed segments are handled, and the hard dependency on the MapMap gateway with no fallback.

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?

Dense and front-loaded, with the core action in the first sentence and parameters, returns, caveats and prerequisites following in order. It is long, but nearly every sentence carries usable information, so only mild trimming is possible.

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?

For a three-parameter matching tool with an output schema and no annotations, the description is complete: inputs, costing semantics, return fields, data-quality caveats and the gateway prerequisite are all covered, leaving no gap an agent would need to fill.

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, but the description adds meaning beyond the schema: the 'provide this or that (not both)' mutual exclusion (not enforced at the top level of the schema), the 2-2000 point range, and an example of how costing changes physical matching (a walk matched as auto snaps to the carriageway).

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?

States a specific verb+resource: snap a recorded GPS trace to the road network and report what was travelled. It clearly separates itself from routing siblings like `route` and `route_observations` by emphasizing matching an already-recorded trace rather than computing a path.

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?

Gives clear context ('turn a dashcam or telematics log into a report') and explains the costing choice that governs matching. It does not explicitly name alternatives or when-not-to-use (e.g. vs `route` or `route_observations`), so it falls short of a 5.

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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