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MapMap

by ai.mapmap

nearby_places

Find places near a point, nearest first with distance in metres — by category (cafes, fuel, EV charging, parking), by name or brand ("the nearest Lloyds bank", "nearest Sainsbury's"), or both. Use this instead of geocode whenever the question is about what is NEAR a location: geocode ranks a brand's branches everywhere and only biases by proximity, so it will happily return a Lloyds in another city over the one 100 m away. Provide lat, lon and at least one of category or name. category is matched against the map's lowercased OSM tag values (amenity/shop/tourism/…), e.g. "cafe", "fuel", "charging_station", "parking", "pharmacy", "supermarket", "hotel", "restaurant", "fast_food", "atm", "bakery", "hospital", "station"; common colloquial names are normalised ("coffee" -> cafe, "ev_charging" -> charging_station, "petrol" -> fuel, "chemist" -> pharmacy). name matches the place's name or alternative names word by word, case- and accent-insensitively, with the last word also matching as a prefix. Combine the two to disambiguate a brand — "Lloyds" plus "bank" excludes Lloyds Pharmacy. A category or name the map does not carry returns an empty list, never an error. Optional radius_m (default 2500, max 100000) bounds the straight-line search distance and limit (default 5, max 10) the result count. Each result has name, one-line label, lat/lon, address parts, distance_m, categories and a details object of display tags (opening_hours, website, phone, ...) when the map carries them. Every result also carries bearing_deg and a spoken direction. Pass heading_deg (degrees clockwise from true north, 0 = north, 90 = east) and results are described from where the user stands — "ahead and slightly to your right, about 80 metres" — with a signed relative_bearing_deg (negative left, positive right); without a heading the phrasing falls back to cardinals ("to the north-east"), so this works with or without a compass. Add fov_deg to keep only what lies within that cone of the heading — it is the FULL width of the cone, so 90 keeps what lies within 45 degrees either side of dead ahead; anything dropped is counted in out_of_view, so a non-zero count means there ARE matching places nearby, just not in front of the user — say that rather than "nothing nearby". Prefer reading direction aloud over coordinates. Requires the MapMap gateway (GATEWAY_URL + GATEWAY_API_KEY).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYesLatitude of the search point in decimal degrees (−90 to 90).
lonYesLongitude of the search point in decimal degrees (−180 to 180).
nameNoThe name or brand of the place to find — "Lloyds", "Lloyds Bank", "Sainsbury's" — for "where is the nearest X" questions. Every word must appear in the place's name or one of its alternative names, case- and accent-insensitively, with the last word also matching as a prefix ("Sains" finds Sainsbury's). Combine with `category` to disambiguate a brand used by more than one kind of place ("Lloyds" plus "bank" excludes Lloyds Pharmacy). Optional when `category` is given; at least one of the two is required.
limitNoMaximum number of results (1–10, default 5).
fov_degNoField of view: the full width in degrees of a cone centred on `heading_deg`, outside which results are dropped — it is the FULL width, so 90 keeps only what lies within 45 degrees either side of dead ahead. Needs `heading_deg` — a cone has to point somewhere. The count of results removed is reported as `out_of_view`.
categoryNoThe kind of place to find. The gateway matches it against the index's lowercased OSM tag values (the value of the POI's `amenity`/`shop`/`tourism`/`railway`/… tag) — e.g. "cafe", "fuel", "charging_station", "parking", "pharmacy", "supermarket", "hotel", "restaurant", "fast_food", "atm", "bakery", "hospital", "station" — and normalises common colloquial names first ("coffee" → cafe; "ev_charging", "ev charging" → charging_station; "petrol" → fuel; "chemist" → pharmacy). A category the index does not carry matches nothing: the result is an empty list, not an error. Optional when `name` is given; at least one of the two is required.
radius_mNoMaximum straight-line distance of any result from the point, in metres (1–100000, default 2500).
heading_degNoWhich way the user is facing, in degrees **clockwise from true north** (0 = north, 90 = east, 180 = south, 270 = west). Supply it and every result is also described from the user's point of view ("just ahead on your right"); omit it and results fall back to cardinal directions ("to the north-east"), so the tool works with or without a compass.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsYesMatching places, nearest first. Empty when the index has no such place within the radius.
out_of_viewNoHow many otherwise-matching places were dropped for falling outside `fov_deg`. Non-zero means there are matching places near the user that are simply not in front of them — say so rather than reporting nothing nearby. Always 0 when no field of view was set.

TDQS

A4.8/5.0
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 delivers: empty-list-not-error, case/accent-insensitive prefix matching, category normalization examples, fov_deg full-width semantics, heading fallback behavior, and out_of_view meaning. It even notes the gateway requirement. Exceptionally transparent.

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 long, but every sentence earns its place given the 8-parameter tool with complex matching/bearing logic. It front-loads the core purpose and then flows into parameter details and edge cases. A bit dense, but structurally organized and appropriately sized for the complexity.

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?

Covers input requirements, output result fields (name, distance, bearing, details), edge cases (empty list, out_of_view), and gateway prerequisites. With an output schema present, it still effectively explains all necessary call parameters and expected behavior. Nothing an agent needs is missing.

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 value beyond schema by clarifying the 'at least one of category or name' requirement (not in required collection), explaining the disambiguation strategy ('Lloyds' plus 'bank'), and mentioning the 'Prefer reading direction aloud' tip. Slight redundancy with schema, but meaningful extra context.

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: 'Find places near a point, nearest first with distance in metres' and immediately scopes by category or name. It distinctly separates itself from geocode by explaining the proximity focus, so an agent can tell it apart from the sibling without ambiguity.

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?

Explicitly instructs to 'Use this instead of geocode whenever the question is about what is NEAR a location' and explains why geocode is unsuitable (returns far branches over nearby ones). This direct comparison gives clear when-to-use guidance with no left to inference.

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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TDQS

A4/5.0
Disambiguation4/5

Most tools target a distinct action and resource pair, and descriptions are explicit about which tool fits which scenario. The closest overlaps—plan_ev_route vs cheapest_charging_along_route, and route vs plan_day vs order_stops vs optimise_routes—are mitigated by clear guidance, so an agent can usually pick correctly.

Naming Consistency4/5

Tool names overwhelmingly follow a verb_noun snake_case pattern (plan_ev_route, set_palette, list_style_layers) with a consistent geo_ prefix for geometry helpers. Minor deviations like elevation, route, and matrix are short and readable but break the strict verb_noun convention.

Tool Count2/5

At 39 tools, this surface is well past the 25+ threshold and feels heavy even for a broad mapping platform. The set spans routing, geocoding, places, styles, EV/fuel, telematics, usage, and feedback, which would be easier for an agent to navigate if split into smaller domain-focused servers.

Completeness4/5

For the stated breadth, coverage is strong: routing, multi-stop planning, VRP, EV/fuel detours, geocoding, places, geometry, style lifecycle, and telematics all have workable primary paths. Minor gaps like no style deletion, no route alternatives, and no batch geocoding are present but do not create dead ends for core workflows.

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