io.github.Unselfisheologism/supercarto
Provides live traffic conditions through a HERE API key, enabling traffic-aware routing with congestion levels, worst-segment reporting, and delays.
Uses OpenStreetMap data via Overpass and PMTiles as live geospatial sources, compiling map features into token-budgeted topological graphs.
Provides live traffic conditions through a TomTom API key, enabling traffic-aware routing with congestion levels, worst-segment reporting, and delays.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@io.github.Unselfisheologism/supercartomap 400m around 35.6833,139.7620 at a 1500-token budget"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
supercarto
mcp-name: io.github.Unselfisheologism/supercarto
LLM-native spatial middleware. Compiles geospatial data into token-budgeted topological graphs that an AI agent can reason over directly.
Give an agent a map without paying the token tax. One file in, both numbers
out: examples/synthetic-dense.geojson is 272,993 bytes (~68,000 tokens) raw
and compiles at a 1024-token budget to about 890 tokens - 26 nodes
(10 named) on 34 edges (streets plus access links), ~99% smaller.
Rerun it yourself: supercarto compile examples/synthetic-dense.geojson --budget 1024. Token counts are character-based estimates on a fixed test
file; the sun and timestamp lines shift them by a few tokens run to run.
npm install supercarto
pip install supercarto # Python client, talks MCP over stdioGive it coordinates
This is the whole product:
import { SuperCarto } from 'supercarto';
const carto = new SuperCarto();
const result = await carto.maplet({
lat: 35.6833,
lon: 139.7620,
radiusM: 400,
budget: 1500,
});
console.log(result.yaml);The same file from the headline, compiled below (truncated output; warnings travel inside the map, where an agent reading only the map sees them):
map:
center: "37.7749, -122.4194"
source: geojson
note: Simplified for reasoning; call expand_feature for exact geometry.
nodes:
n530: {type: poi, name: Bank 0, tags: [bank]}
n531: {type: poi, name: Cafe 1, tags: [cafe]}
n673: {type: transit, name: Station 0, tags: [subway_entrance]}
edges:
n515->n516: {dist: 80m, dir: east, path: road}
n516->n517: {dist: 80m, dir: east, path: road}
omitted:
- {layer: building, count: 259}
- {layer: poi, count: 141}
- {layer: road, count: 441}
tools:
- expand_feature(id)That is about 890 tokens for the whole extract at a 1024 budget: every drop named by layer, nothing silently vanished.
Related MCP server: ThinAir Geo
The problem
You want an agent to know where things are and how to get between them. Three options, all bad:
Paste GeoJSON into the prompt. Thousands of tokens per block. The model reasons badly about raw coordinates and will hallucinate them.
Call a map API. You get back the same GeoJSON. The token cost is unchanged; only the HTTP round trip differs.
Invent a compact wire format and teach the model to read it. A novel grammar in a context window produces hallucinated coordinates and tokens burned inferring the grammar. A model was not trained on your format.
supercarto takes the third option seriously and solves it the only way it can be solved: the model never reads the wire format.
coordinates live sources reasoning format
35.68, 139.76 ──▶ Overpass / PMTiles ──▶ topological YAML graph
distances in metres,
compass directions,
built for modelsThe wire format is a spec you own, optimized for machines. The reasoning format is standard YAML in flow style, using only constructs a model has read billions of times. A deterministic compiler in the middleware does the translation.
An agent works for a person
The person is standing outside. Rain and traffic change whether the trip happens at all, so those are first-class layers here, not an afterthought bolted onto a routing engine.
Weather
No API key. No account. No billing.
const w = await carto.weatherAt(35.6833, 139.7620, 12);
w.now.summary; // "heavy rain"
w.now.precipitation; // "rain" | "snow" | "sleet" | "hail" | "drizzle"
w.now.feelsLikeC; // what they actually feel$ supercarto weather --lat 35.6833 --lon 139.762
now clear night, 23.2C (feels 26.9C), wind 4.3km/h, precip none
2026-10-01T13:00:00Z 22.7C none 3.5km/h
2026-10-01T14:00:00Z 22.1C none 3.1km/hThe MCP tool returns prose rather than fields, because a model repeats "it is raining, take the bus" reliably and re-derives it from twelve numbers unreliably.
When the source is unreachable it returns NaN for every reading and sets
degraded, and the tool raises. It does not return zeroes. An agent handed
precipitationMm: 0 concludes the weather is dry and sends someone cycling into
a storm.
Traffic
const t = await carto.routeWithTraffic({ from, to, mode: 'drive' });
t.freeFlowOnly; // true when no flow credential is configured
t.worstLevel; // "severe" - the segment that makes someone late
t.delayS;Traffic needs a credential, so get_traffic is absent from the tool list
until you set one. With no key the routing path reports free-flow and labels it:
$ supercarto traffic --from 37.7749,-122.4194 --to 37.7849,-122.4194
traffic: unknown (no flow source configured)
free-flow estimate: 244s, ignores congestion
note: set TOMTOM_API_KEY or HERE_API_KEY for live conditionsA free-flow estimate for a congested route is worse than no answer, because the person leaves on the schedule the agent gave them. Presenting one as live is the single most damaging thing this library could do, so it does not.
Set TOMTOM_API_KEY or HERE_API_KEY and it becomes live, reporting the worst
segment rather than an average.
Indoor floors
Nobody else publishes this. Google builds indoor maps for its own users and has no reason to hand a third-party agent a clean floor-by-floor graph; Mapbox has nothing comparable; every MCP wrapper returns the same flat GeoJSON.
indoor:
storeys: 0,1,2
multi_storey: true
0: {label: ground, rooms: 12}
L0n1: {type: corridor}
L0n2: {type: shop, name: "Blue Bottle"}
L0n1->L0n2: {dist: 11m}
v1: {type: lift, levels: [0,1,2], wheelchair: true}
missing: [1]Lifts are collapsed across storeys into one vertical link. Rooms only connect to
corridors on the same floor — an edge through a concrete slab would send
someone through a floor. A room more than 40m from any corridor is left
unattached, because a straight line through intervening walls is worse than no
edge. Untagged storeys are reported as missing rather than invented.
Transit schedules
Set SUPERCARTO_GTFS to any static GTFS feed (an unzipped directory or a .zip)
and get_transit appears: stops, lines, and a service window for the queried
day. It is the one agent map tool that answers "can you be somewhere by 9"
from an actual schedule, and every agency's failure mode is covered by an
honest state rather than a guess:
transit:
center: "36.913, -116.77"
radius: 3.0km
feed: sample-feed
note: Stop distances are straight-line. Schedule from GTFS feed for 2007-01-11 in America/Los_Angeles.
nodes:
STAGECOACH: {type: stop, name: "Stagecoach Hotel & Casino", tags: [bus], routes: ["30", "40"], first: 06:00, last: 06:58}
NADAV: {type: stop, name: "North Ave / D Ave N", tags: [bus], routes: ["40"], first: 06:14, last: 06:44}
edges:
NADAV->DADAN: {dist: 601m, dir: south, path: bus 40}Three service states, never two. Running today shows routes and first/last departures. A date with nothing running keeps the stops with
routes: []— no transit is not no stops — and the note says so. A feed without a calendar shows the whole network and says the service window is unknown instead of presenting whole-feed data as today's schedule.Times past midnight stay past midnight. GTFS expresses a departure after the service day's midnight as hours beyond 24 (
25:10). Rendering that as01:10fits the eye and misleads the decision, so it renders as recorded, in the feed's own timezone.Edges are structure; schedules attach to nodes. The adjacency survives a no-service day because an agent walking between stops still wants it.
Live delays when you point GTFS-Realtime at it. Set
SUPERCARTO_GTFS_RTto the agency's TripUpdates URL and predictions join onto the schedule: node lines gain alive:attribute such asbus 16 +300sorbus 16 cancelled, and the note says how fresh the feed is. Spec precedence holds — an explicit stop update beats the trip-level delay; absolute-time predictions convert through the feed's timezone. The honesty rule that matters most: a stop with no live entry is unknown, not on time, and the note says so in words. A feed that will not read (404, timeout, malformed data) degrades to the pure schedule answer with a one-sentence explanation, never a silent drop.Next departures, not just the day's window. A
next:attribute on each node lists the upcoming departures at that stop, in feed time order and in the feed's clock (tram 9 25:10stays past midnight), capped at three with the hidden rest counted (+2 more). With a realtime feed configured the board speaks the prediction inline —bus 16 07:05 +300sorcancelled— and thelive:list omits anything the board already said, so nothing prints twice. When the day's last departure has run, the note says the day is over instead of letting an empty board read as a broken feed.A certified ride plan between two coordinates.
get_transit_routewalks to a stop, rides one line that serves both catchments, and walks it off — all straight-line legs labelled as such, never an interchange the feed cannot prove. Plans ride on the departure board's honesty: certified against the service day, predictions decorate their trips, cancelled trips drop out with a counted note, and when nothing can be certified the note names the leg that failed.The signal is explicit. Departure times, the schedule date, and the feed's timezone travel in the
note, where a model reading only the map sees them.
The budget is a real budget
Most map-for-agents code truncates and hopes. supercarto treats truncation as a correctness problem:
Ranking. Named landmarks survive; anonymous intersections are cut first.
Connectivity. Cut vertices are never removed, so the surviving network stays connected. A map the agent can traverse but not connect is worse than a smaller map it can use.
No stranded names. A landmark the agent can see but cannot route to is removed. It would confidently try to walk to it.
Verification.
emitAndVerifyre-measures the actual emitted YAML and cuts again until it fits. The reported budget is the budget delivered, not an estimate.Honesty. Every drop is recorded in
omitted:, aggregated per layer, so the model can tell "not there" from "not shown".
The benchmark
Lives in its own repo now: supercarto-bench.
Same question, same model, same map — the only variable is what sits in the context window: compiled graph vs raw GeoJSON vs nothing. Raw runs, frozen test maps, and the scoring code all live there, so anyone building a rival map tool can run the same test and try to beat the score.
Current headline there: 9/9 for the compact graph against ~4/9 raw and 0/9
with no map, on two AI models. Route questions are unmeasurable (the public
test router gives impossible answers) — so route is demoted to a
feasibility answer: straight-line distance, compass direction, then
"verify the approach on arrival". Turn-by-turn requires a router you host and
have verified; OSRM is opt-in, not default. Stated, not hidden.
The score now ships with its own reproduction recipe (scoreC certificate,
re-derived from raw answer archives without model calls), a per-model
leaderboard, and adapter arms: a rival tool registers itself with a single
{ name, prepare } module and gets the same tasks, models and pass rules.
The current certificate: supercarto 38/47 · geojson 15/44 · none 0/47
across every archived run.
Live certificate — every number, re-derivable from committed archives with no model calls.
Live leaderboard — per model and per arm, including the free models you can run yourself.
What this repo keeps is a quick token-cost check:
npx supercarto bench <file.geojson>Data sources
Source | Status | Use |
Overpass | default | Development. 15-40s per call, rate limited. |
PMTiles / Protomaps |
| Production. Range requests against object storage, milliseconds, no rate limit. |
OSRM | default | Turn-by-turn routing, walk/bike/drive. |
Open-Meteo | default | Weather. No key. |
TomTom / HERE | key required | Live traffic. |
Terrarium tiles | default | Elevation and slope. No key. |
GTFS feeds |
| Transit stops, lines, and schedules. Any static feed, no key. Live delays join from a GTFS-Realtime TripUpdates URL via |
export SUPERCARTO_PMTILES=https://your-bucket/planet.pmtilesPMTiles is the answer at production volume. The reader is included — no pmtiles
dependency, no mapbox-gl, zero transitive packages.
Everything is read from the environment by one factory, so the CLI and the MCP server cannot disagree about where data came from. They did once, and nothing reported it.
SUPERCARTO_PMTILES PMTiles archive URL; primary source when set
OVERPASS_ENDPOINT your own Overpass instance
TERRARIUM_ENDPOINT elevation tile template
TOMTOM_API_KEY enables get_traffic
HERE_API_KEY enables get_traffic
SUPERCARTO_NO_TERRAIN disable elevation
SUPERCARTO_NO_WEATHER disable weather
SUPERCARTO_GTFS path to a GTFS feed (dir or .zip); enables get_transit
SUPERCARTO_GTFS_RT TripUpdates URL; adds live delay lines to get_transitWhole-metro feeds load entirely into memory. Since 0.6.1 the load is
columnar — stop_times go straight into per-trip arrays, no row objects — so
a feed the size of Translink SEQ (35 MB zip) fits Node's default heap and
the answer is byte-identical to the pre-0.6.1 build. Verified live at that
size: schedule, departure boards, and 750+ realtime predictions join in one
answer with no NODE_OPTIONS at all. Bigger feeds load in proportion to
their own inflated size; give such a run NODE_OPTIONS=--max-old-space-size=4096.
Agent integration
MCP
{
"mcpServers": {
"supercarto": { "command": "npx", "args": ["supercarto", "mcp"] }
}
}Seven tools: get_maplet, route, search_places, expand_feature,
get_daylight, get_terrain, get_weather, plus get_traffic when a key is
present, get_transit and get_transit_route when a GTFS feed is
configured. Each maplet lists exactly the tools that deployment can answer,
so an agent never calls one that cannot work. get_daylight is always
available: sun position is arithmetic, needing no source and no credential.
What a maplet says about a place
Beyond topology and distance, a node can carry the facts that decide whether a trip is worth making:
n3: {type: poi, name: Blue Bottle, tags: [cafe], hours: 07:00-18:00, brand: Blue Bottle}
n4: {type: poi, name: Central Rx, tags: [pharmacy], hours: 09:00-17:00}
map:
sun: up, elevation: 43.4deg, twilight: day, azimuth: 22deg, sunrise: "..."Both shops above are open at 12:00 local. At 04:00 the same two read
hours: closed, because their hours are known and they are shut. A bench with
no recorded hours emits no hours field at all.
hours is evaluated against an instant and evaluated in the map's timezone,
not the server's. Three states, never two: a time range, closed, or absent
because nobody recorded it. Absent is deliberately not closed — those lead to
opposite advice, one means go and find out, the other means go elsewhere.
twilight is day, civil, nautical, or night rather than a boolean,
because the three lead to different decisions: walking is fine at civil
twilight and unwise at nautical. Above the Arctic circles in summer and winter
the map reports polar: day or polar: night instead of inventing a sunrise.
Pass compile.utcOffsetMinutes and compile.now to fix both to a known place
and instant, which is what the tests do.
Python
import asyncio
from supercarto import SuperCarto
async def main():
async with SuperCarto() as carto:
m = await carto.maplet(lat=35.6833, lon=139.7620, radius_m=400, budget=1500)
print(m.yaml)
w = await carto.weather(lat=35.6833, lon=139.7620)
asyncio.run(main())Zero runtime dependencies. It speaks JSON-RPC to the Node server over stdio
rather than importing an SDK, because the mcp package pulls in httpx and
pydantic and an agent framework that already depends on one of them should not
get a second copy.
HTTP
curl 'localhost:8787/maplet?lat=35.6833&lon=139.762&radiusM=400&budget=1500&format=yaml'Hosted mode (deployed)
The compile path is Workers-compatible (fetch-based sources, no Node built-ins),
and cloud/worker/ packages it as a Cloudflare Worker: the same /maplet,
/scr, weather, daylight and terrain surface as the local HTTP API above, with
per-IP rate limiting and ODbL attribution on every map response. It is
deliberately a subset — transit and traffic are not served, because a Worker has
no filesystem for a GTFS feed and hosted mode ships no flow-provider keys, and
GET / states that openly. See cloud/worker/README.md.
Deployed at https://supercarto-compile.jeffrinjames99.workers.dev. Data source
chain for /maplet and /scr: live Overpass first, then a hosted PMTiles
planet build of the OpenStreetMap basemap — every known public Overpass
instance refuses the shared Cloudflare egress range (HTTP 521), so the
fallback is what actually serves a hosted maplet. The archive's detail level
is coarse beyond z10-ish, and the deployment caps a request's tile and
feature fetches so a planet tile stays inside a Worker CPU budget;
truncation travels in the document as warnings, and the source: line says
protomaps when the fallback served it. Weather, daylight and rate limiting
are live; terrain answers an honest 503 (Terrarium tiles are PNG-encoded and
the bundled zero-dependency decoder reads raw Terrarium buffers only); transit
and traffic are absent for the reasons GET / lists. GET /healthz probes
Overpass only, so a 503 there means the Overpass leg is unreachable — not that
the PMTiles fallback is down.
CLI
supercarto fetch --lat 35.6833 --lon 139.762 --radiusM 400 --budget 1500 --metrics
supercarto weather --lat 35.6833 --lon 139.762 --hours 12
supercarto traffic --from 37.7749,-122.4194 --to 37.7849,-122.4194
supercarto transit --feed city-gtfs.zip --lat 36.913 --lon -116.77 --radiusM 3000
supercarto route --from 37.7749,-122.4194 --to 37.7849,-122.4194 --mode walk
supercarto mcp
supercarto serve --port 8787
supercarto compile city.geojson --budget 1024 --metrics
supercarto encode city.geojson --out city.scr
supercarto decode city.scr
supercarto bench city.geojsonfetch layers accept road and poi alongside raw OSM tags; anything
else is rejected rather than silently returning nothing. A request that
would yield named places with no streets fails with a clear error instead
of a misleading map — pass --layers poi for a POI-only map. Warnings
travel inside the map output, not just stderr, so agents see them.
Accuracy
Distances are verified against haversine ground truth at mid and high latitude:
0.11% error at San Francisco (37.77°N)
0.11% error at Oslo (59.91°N)
This required getting the Web Mercator scale correction right: mercator distances
exceed ground distance by 1/cos(latitude) on both axes, and getting the sign
wrong produces a 26% error at mid latitude that compounds toward the poles. A
mean isotropic scale is also wrong for a non-square bbox, so the scale is applied
per axis.
The benchmark task set spans 64°N to 33°S deliberately. A benchmark run only at 37°N measures one case and hides every sign error toward the poles.
Status
461 tests passing: 328 unit (including 42 for the GTFS transit layer, 14 for the protobuf wire and TripUpdates schema, 5 for the real-time overlay join, and 7 for the PNG elevation decoder), 121 against live sources, and 12 MCP end-to-end. Outputs are byte-identical to the Python port - the parity engine runs on every push. Verified against live OpenStreetMap, live Open-Meteo, and the live MCP server driven from Python.
Implemented: SCR codec (text and binary, delta geometry, ring groups, three heat encodings, omissions, routes, refs), Web Mercator with per-axis scale correction, Visvalingam-Whyatt simplification, GeoJSON ingestion with OSM classification, topology compilation with junction welding and landmark attachment, token budgeting with connectivity guarantees, YAML emission, indoor floor graphs with vertical circulation, place-density heatmaps, live Overpass, PMTiles with a built-in MVT decoder, routing demoted to feasibility (straight-line distance and direction by default; OSRM turn-by-turn is opt-in for an engine you have verified — the public demo router returns impossible distances), keyless weather with a degraded path that refuses to invent readings, traffic with an honest free-flow fallback, terrain elevation and slope, GTFS transit schedules with three honest service states, GTFS-Realtime trip updates joined onto those schedules with unknown-not-on-time semantics, MCP over stdio, HTTP API, CLI, a Python client, and a task-level benchmark with OSRM ground truth.
Not implemented: hosted vector tiles with style delivery, and full indoor room geometry beyond centroids.
License
Apache-2.0
Available Tools
8 toolsexpand_featureA
Fetch the exact geometry of features by id, as WKT. Use only when shape matters, such as checking whether a route clears a building.
| Name | Required | Description | Default |
|---|---|---|---|
| ids | Yes | ||
| scr | Yes | SCR document from a previous get_maplet. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It discloses that this is a read/fetch operation and that the response is WKT geometry, but says nothing about permissions, error behavior for unknown IDs, or whether it returns one or many geometries.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, front-loaded with the action and output format, followed by a tightly scoped usage condition and example. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter read tool with no output schema and no annotations, the description adequately covers what it returns (WKT geometry) and when to use it. Minor gaps remain around where IDs come from and error handling, but the core invocation context is present.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50%: the 'scr' parameter is documented in the schema, while 'ids' is undocumented there. The description adds 'by id' and clarifies the fetched result is exact geometry, but does not explain the ID source or relationship to the SCR document.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ('Fetch the exact geometry of features by id') and adds the output format ('as WKT'). It also implicitly distinguishes itself from map-display siblings by saying to use it only when shape matters, so an agent can select it without opening other schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives explicit usage guidance with 'Use only when shape matters' and a concrete example ('checking whether a route clears a building'). It does not name the alternative tool to use for non-shape queries, so it stops short of fully explicit routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_daylightA
Sun position, sunrise, sunset, and twilight state for a coordinate. Twilight is reported as day, civil, nautical, or night rather than a boolean, because "needs a torch" and "cannot see" are different states.
| Name | Required | Description | Default |
|---|---|---|---|
| at | No | ISO 8601 instant. Defaults to now. Times are UTC. | |
| lat | Yes | ||
| lon | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, so the description carries the burden. It discloses the shape of the return conceptually (categorical twilight states rather than a boolean) and explains why, which is genuinely useful, but it says nothing about whether times are returned as instants vs. local strings, what 'sun position' looks like numerically, or any rate/permission constraints. Read-only is inferable only from the 'get_' name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the returned quantities, then a short design note that prevents a wrong expectation (boolean vs. four-state twilight). Nothing is padded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description does well to enumerate the returned quantities, but an agent still cannot predict the serialized form — time format of sunrise/sunset, units for sun position, or field names. For a 3-parameter lookup returning multiple values, that is a real gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 33% — only 'at' is documented in the schema. The description adds only 'for a coordinate', which maps lat/lon to location but gives no sign convention, unit, or precision guidance, and never mentions the optional 'at' override or UTC/Earth-time handling.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a concrete resource ('sun position, sunrise, sunset, and twilight state') with its required input ('for a coordinate'), which makes it unmistakable against siblings like get_weather and get_terrain. The clarifying note about how twilight is reported reinforces exactly what this tool returns.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is only implied: 'for a coordinate' and the twilight rationale hint at daylight/visibility decisions, but there is no explicit when-to-use, when-not-to-use, or named alternative (e.g., get_weather for cloud cover that would make twilight moot).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_mapletA
Fetch real map data around a coordinate and return a token-budgeted topological graph: named places joined by walkable edges with distances in metres and compass directions. Use this to answer 'what is near X' and 'how do I get from A to B'. Omissions are reported, so treat a missing feature as not-shown rather than absent.
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | Latitude in WGS84 degrees. | |
| lon | Yes | Longitude in WGS84 degrees. | |
| budget | No | Token budget for the response. Default 1024, hard cap 8192. | |
| layers | No | Semantic layers to include, e.g. ["road", "poi"]. Default all. | |
| radiusM | No | Radius in metres. Default 300. Larger costs more tokens. | |
| returnScr | No | Also return the wire SCR in a second content block. Keep it: it is the session state a later localize() call moves within, and the agent stores it in its own RAM rather than the chat context. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden and does well: it discloses that output is token-budgeted, that results form a topological graph, and that omissions are reported so missing features should be treated as not-shown rather than absent. It does not explicitly state read-only/no side effects, but 'Fetch' strongly implies it.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with the core action and output shape, then use cases, then an important caveat about omissions. Every sentence earns its place without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a six-parameter graph-fetch tool with no output schema and no annotations, the description explains the returned graph, edge attributes, token budgeting, and omission semantics. It could say more about response structure or layer effects, but what is present is sufficient for an agent to understand the call's result and caveats.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all six parameters in detail, including defaults, token cap, layers, radius, and returnScr. The description mentions token budgeting but does not add parameter-level meaning beyond what the schema provides, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description gives a specific verb and resource: fetch real map data around a coordinate and return a token-budgeted topological graph. It clearly states the key output elements, named places joined by walkable edges with distances and directions, but does not explicitly distinguish this from siblings such as route or search_places.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives explicit use cases: answering 'what is near X' and 'how do I get from A to B'. However, it provides no when-not-to-use guidance and does not name alternatives like route or search_places, leaving the sibling routing overlap unresolved.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_terrainB
Sample ground elevation around a coordinate, in metres. Returns the range plus high and low points, which is what an agent needs for grade reasoning.
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | ||
| lon | Yes | ||
| radiusM | No | ||
| samples | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden; it does disclose units (metres), the sampling concept, and the return shape (range plus high/low points). It omits important behavioral caveats such as data resolution/accuracy, cost or rate limits, and behavior when the coordinate falls outside coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences with no filler; the core action and result are front-loaded in the first clause. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, and the description gives only a one-line sketch of the return values, which is roughly adequate. However, for a four-parameter geo tool with zero schema descriptions, the missing parameter and data-quality detail leaves the definition only minimally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% for four parameters, so the description must compensate. It vaguely gestures at a 'coordinate' and sampling 'around' it, but never names lat/lon, never explains that radiusM is in metres with a 1000 m default, and says nothing about the 'samples' count or its default of 16 — 'in metres' is even ambiguous between elevation and radius.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ('Sample ground elevation around a coordinate') plus the output shape (range, high and low points). It is clearly distinct from siblings like route, get_weather, or get_daylight, though it never explicitly contrasts with them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'which is what an agent needs for grade reasoning' implies the intended use case, but there is no explicit when-to-use/when-not-to-use guidance and no mention of alternatives for elevation-adjacent needs. Usage is inferable rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_weatherA
Current weather conditions and a short forecast at a coordinate. Use this before telling someone to walk, cycle, or drive, or to pick between an indoor and outdoor option. Reports what is actually falling (rain, snow, sleet) rather than only a temperature.
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | ||
| lon | Yes | ||
| hours | No | Forecast horizon in hours. Default 12, max 48. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It usefully discloses the character of the data ('reports what is actually falling... rather than only a temperature'), but says nothing about permission needs, units, failure modes, or what the response contains.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences: purpose front-loaded, then usage trigger, then a differentiator. No filler, and each sentence adds a distinct piece of information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 3-parameter, no-annotation, no-output-schema tool, the description covers purpose and usage but leaves coordinate units, forecast horizon, and returned data shape unaddressed. Adequate to select the tool, thin for calling it precisely.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 33%, so the description should compensate but mostly doesn't. 'At a coordinate' implies lat/lon without specifying units or format, and it never mentions the hours horizon parameter, whose default/max is only documented inside the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The first sentence gives a specific verb+resource+scope: current conditions and a short forecast at a coordinate. It is clearly distinct from terrain or daylight siblings by domain, but it never names or contrasts a sibling explicitly, so it stops short of the top score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It states concrete triggering situations (before recommending walk/cycle/drive, or choosing indoor vs outdoor), which is clearer than typical implied usage. It offers no when-not guidance and does not point to an alternative tool for other weather needs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
localizeA
Move within a map session you already hold. Pass the session SCR (from get_maplet with returnScr, or from the last localize) and your new position; the answer is only the delta: + places that entered view, - places that left it, ~ places that moved or updated. Store the updated SCR from the second content block and discard the old one.
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | Latitude in WGS84 degrees. | |
| lon | Yes | Longitude in WGS84 degrees. | |
| scr | Yes | Session state: the SCR string from the previous get_maplet or localize. | |
| budget | No | Token budget for the re-fit view. Default 1024, hard cap 8192. | |
| layers | No | Semantic layers to include, e.g. ["road", "poi"]. Default all. | |
| radiusM | No | Radius in metres. Default 300. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well: it discloses the state-mutation lifecycle (store the updated SCR from the second content block, discard the old one) and the return semantics (delta only: + entered, - left, ~ moved/updated). It does not cover error behavior or what happens if the SCR is stale, keeping it from a 5.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three tight sentences, front-loaded with the core action and ending with the critical state-maintenance instruction. Every clause carries operational value with no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
It compensates for the missing output schema by explaining the delta return format and the two-content-block structure, and covers the important stateful behavior. Minor gap: the optional budget/layers/radiusM parameters are not mentioned, though the schema documents them fully.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents lat/lon, scr, budget, layers, and radiusM. The description adds a little beyond the schema by emphasizing the SCR rotation rule, but adds no syntax or format detail for the geographic or budget parameters; baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb+resource ('Move within a map session') and distinguishes it from get_maplet by describing it as motion inside an existing session. An agent can tell it apart from the session-creating sibling without opening the schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly frames it as operating on a session 'you already hold' and names where the SCR comes from (get_maplet with returnScr, or the last localize), which implies the correct sequencing. It stops short of explicitly saying not to use this for an initial fetch or how it differs from route, but the prerequisite is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
routeB
Feasibility between two coordinates: straight-line distance, the compass direction to head, and nothing that pretends to be turn-by-turn navigation. Turn-by-turn comes only from a routing engine the operator has verified; the default answer ends in "verify the approach on arrival", which is the honest limit of what it computes.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | walk | |
| toLat | Yes | ||
| toLon | Yes | ||
| fromLat | Yes | ||
| fromLon | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it does disclose one genuinely useful behavioral trait: the default answer appends a 'verify the approach on arrival' caveat, and no turn-by-turn data is computed. However, it says nothing about units, coordinate ordering assumptions, error behavior, or what the 'mode' parameter actually changes if distance is straight-line.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The core capability is front-loaded in the first clause, which is good, but the second half is editorial prose that restates the same disclaimer about turn-by-turn navigation twice. It is not bloated enough to bury the point, but sentences could be tightened.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations and no output schema, the description is the only source of truth, and it does convey the substance of the return (distance, direction, a caveat string). It still leaves gaps an agent needs for correct invocation: units, response format, and the effect of mode.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% across five parameters, so the description must compensate. It only implies that fromLat/fromLon and toLat/toLon form the two coordinate pairs; it never explains units, precision, ordering, or the role of the walk/bike/drive enum, which is left entirely opaque.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a concrete output set: straight-line distance and compass bearing between two coordinates. It sharply disambiguates itself from a true routing engine by denying turn-by-turn capability, so an agent knows exactly what it will and will not get. It does not distinguish itself from any sibling tool by name, which keeps it below a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied rather than stated: the word 'Feasibility' and the denial of turn-by-turn suggest a quick sanity check, not navigation. There is no explicit 'use this when…, use X instead' guidance, and no sibling (get_maplet, localize, search_places) is named as an alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_placesA
Find named places (cafes, stations, shops) near a coordinate. Cheaper than get_maplet when only names are needed.
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | ||
| lon | Yes | ||
| limit | No | ||
| query | No | Substring match on place name or type. | |
| radiusM | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral burden; it does disclose the cost tradeoff versus get_maplet, which is genuinely useful. It says nothing about return shape (names only, or with type/distance?), result ordering, or how radius/limit affect recall, so an agent cannot predict output.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences, zero filler, with the core purpose front-loaded and the sibling comparison as a secondary clause. Every phrase earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 5-parameter tool with no annotations and no output schema, the description covers purpose and the sibling tradeoff but leaves output content and the radius/limit behavior unexplained. Adequate to call the tool, not adequate to predict what comes back.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 20% — just the `query` property is documented — and the description does not compensate. It never mentions that limit defaults to 20 or that radiusM defaults to 300m, a value that materially constrains results; only lat/lon are implied by "near a coordinate".
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Specific verb (Find) plus resource (named places) with concrete examples and a spatial scope (near a coordinate). It also explicitly distinguishes itself from get_maplet, so an agent can pick between them without opening a schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
"Cheaper than get_maplet when only names are needed" names the alternative and the condition that selects it, implying get_maplet is for richer detail. It gives no guidance on the other siblings (route, localize, expand_feature), but the core routing decision is covered.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
8 tool updates
v0.1.0- First observed
expand_feature - First observed
get_daylight - First observed
get_maplet - First observed
get_terrain - First observed
get_weather - First observed
localize - First observed
route - First observed
search_places
TDQS
Scored across 8 tools
Most tools have clearly distinct purposes, but route and get_maplet both mention answering 'how do I get from A to B', which could cause misselection. search_places also overlaps with get_maplet's near-coordinate lookup, though the descriptions clarify cost and output shape.
Most names follow a verb_noun pattern (get_maplet, search_places, expand_feature, get_terrain, get_weather, get_daylight), while route and localize are bare verbs. The convention is still readable and mostly predictable.
Eight tools is well-scoped for a geo/spatial reasoning server. Each tool covers a distinct capability area: routing, map graph retrieval, session localization, place search, geometry expansion, terrain, weather, and daylight.
The surface covers map data, place search, geometry, terrain, weather, daylight, and session-based movement, which is strong lifecycle coverage for agent navigation. Minor gaps remain: no address/place-name geocoding to coordinates and routing is explicitly limited to feasibility rather than turn-by-turn.
Maintenance
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