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by ai.mapmap

check_adr_tunnel

Check whether a vehicle may pass through a tunnel of a given ADR category ("A"–"E"). Provide hazmat and, when known, the load's ADR 8.6.4 tunnel restriction code (e.g. "B", "C5000D", "B/D", "none"). Applies the conservative worst-case reading: conditional clauses are assumed to apply, so a blocked answer may over-restrict but never under-restricts. No network access; answers instantly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hazmatYesWhether the vehicle carries dangerous goods at all. When false the ADR tunnel matrix does not apply and every tunnel is permitted.
tunnel_codeNoADR 8.6.4 tunnel restriction code of the load, e.g. "B", "C5000D", "B/D", or "(—)"/"none". Leave unset for a hazmat load of unknown code (conservatively treated as code B).
tunnel_categoryYesADR category of the tunnel to check: "A", "B", "C", "D" or "E".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonNoWhy passage is forbidden (present only when blocked); cites the ADR 8.6.4 rule that applied.
decisionYesWhether passage is allowed or blocked.
explanationYesHuman-readable explanation of how the decision was reached, including the conservative worst-case reading.
forbidden_tunnel_categoriesYesADR tunnel categories this load is forbidden from under the worst-case reading (conditional clauses assumed to apply). Empty when unrestricted.

TDQS

A4.2/5.0
Behavior5/5

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

With no annotations provided, the description carries the full disclosure burden and succeeds: it states the conservative worst-case reading ('a blocked answer may over-restrict but never under-restricts') and operational traits ('No network access; answers instantly'). This is exactly the kind of behavioral context agents need beyond the schema.

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?

Three sentences, purpose front-loaded in the first ('Check whether a vehicle may pass through a tunnel...'), with every sentence earning its place. No fluff; the behavioral caveat and operational notes are tightly packed in the final sentence.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, parameter usage, interpretation semantics, and operational behavior, while the output schema covers return values and the input schema covers all parameters. Slightly more could be said about edge-case handling (e.g., unrecognized tunnel codes), but nothing essential to selecting or invoking the tool correctly is missing.

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 already documents all three parameters; baseline is 3. The description adds marginal value by reinforcing which parameters to supply and giving code examples ('B', 'C5000D', 'B/D', 'none'), though these overlap with the schema's own examples.

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 and resource: 'Check whether a vehicle may pass through a tunnel of a given ADR category ("A"–"E")'. The ADR tunnel category scope clearly differentiates it from geo siblings like check_clearance_on_route, which concerns route clearance rather than hazmat tunnel restrictions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies its use case (determining hazmat tunnel passage) and instructs what to provide ('Provide hazmat and, when known, the load's ADR 8.6.4 tunnel restriction code'), but it never explicitly contrasts with alternatives such as check_clearance_on_route or states when not to use it. Usage context is present but 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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