ThinAir Geo

resolve_intersection

Read-onlyIdempotent

Resolve a NAMED cross-street (street1 × street2 within a locality/region/country) to a single best coordinate with a confidence score and a fleet-safe fallback. High-confidence tier matches US-metro highway corners; named non-highway streets resolve via the geocoder fallback. For free-form streets use geocode.

Input Schema

TableJSON Schema

Name	Required	Description
`region`	No	State / province (full name or abbreviation, e.g. 'TX')
`country`	No	ISO 3166-1 alpha-2/alpha-3 country code
`street1`	Yes	First street of the cross-street pair
`street2`	Yes	Second (cross) street of the pair
`locality`	No	City / town that scopes the lookup

Output Schema

TableJSON Schema

Name	Required	Description
`meta`	No
`admin`	No
`feature`	Yes	Best resolved intersection feature, including validated optional intersection provenance, or null when nothing resolved
`warnings`	No
`precision`	No
`confidence`	Yes	Resolved confidence 0–1 (0 when nothing resolved)
`fallback_used`	No	True when the geocoder fallback produced the answer
`upstream_error`	No

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already provide read-only and idempotent hints, so the bar is lower. The description adds useful behavioral details: confidence score, fleet-safe fallback, and tiered matching for US-metro highways versus geocoder fallback for non-highway streets.

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?

Two sentences, front-loaded with purpose, no fluff. Every clause adds value, including the fallback behavior and sibling differentiation.

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?

Given the annotations and output schema presence, the description adequately covers the tool's purpose, behavioral nuances, and alternative use cases. It could mention the return format in more detail, but output schema likely covers that.

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 covers 100% of parameters with descriptions, so baseline is 3. The description adds minimal extra meaning, reinforcing that inputs are street names scoped by locality/region/country, but does not materially enhance understanding beyond the schema.

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 clearly states the tool resolves a NAMED cross-street to a coordinate with confidence score and fleet-safe fallback. It distinguishes from the geocode sibling by explicitly saying 'For free-form streets use geocode.'

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?

Provides explicit when-to-use guidance: use for named cross-streets, use geocode for free-form streets. Also describes internal fallback behavior, giving context on tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All ThinAir Geo tools Inspect

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TDQS

A4.4/5.0

Disambiguation5/5

Every tool has a clearly distinct purpose. Overlapping tools (geocode vs geocode_structured, explore vs search_places, map_match vs trace_attributes) are explicitly differentiated in their descriptions, with cross-references guiding correct selection. No two tools appear to do the same thing.

Naming Consistency4/5

Most tools follow a predictable verb_noun pattern (geocode, reverse_geocode, batch_geocode, search_places, resolve_intersection), but there are minor deviations: some tools use noun-only names (directions, traffic, weather) and a couple use noun_verb order (place_get, geofence_contains). This creates slight inconsistency but remains readable.

Tool Count4/5

At 19 tools, the set is slightly heavy but each tool earns its place by covering a distinct geospatial function. The breadth spans geocoding, routing, places, telematics, geofencing, traffic, weather, and account management, making the count reasonable for a comprehensive geo API.

Completeness5/5

The tool surface is remarkably complete for a geospatial server: forward, reverse, batch, structured, and intersection geocoding; routing, matrix, and isochrones; place search, exploration, and ID lookup; map matching, trace attributes, and point snapping; plus geofencing, traffic, weather, and quota management. No critical gaps or dead ends apparent.

Resources

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