Skip to main content
Glama
malonestar

gov-data-mcp

by malonestar

faa-drone-airspace-checker

Check drone flight coordinates against FAA airspace data, returning LAANC ceilings and restriction verdicts, with gated results when layers are unavailable.

Instructions

FAA Drone Airspace Checker - Batch LAANC & No-Fly Verdicts. Batch lat/lon to FAA UAS airspace verdicts: LAANC ceilings AND whether LAANC is actually offered, charted Class B/C/D/E surface areas, prohibited areas, national-defense TFR areas, special use airspace, NSUFR, stadium TFRs. Verdict is gated - it never reads clear when a layer did not answer. Reads live from the official government source. Store page: https://apify.com/malonestar/faa-drone-airspace-checker

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
layersNoWhich FAA airspace layers to check each point against. Defaults to all eight. laanc_grid = UAS Facility Map LAANC ceilings; class_airspace = charted Class A/B/C/D/E airspace (ALWAYS queried - it is the check that distinguishes uncontrolled airspace from controlled airspace that has no LAANC grid, so excluding it would force every verdict to INCONCLUSIVE); prohibited_areas = P-areas like P-56; national_defense_tfr = national defense airspace TFR areas; special_use_airspace = Restricted/MOA/Alert/Warning/Danger areas; part_time_nsufr = part-time national security UAS flight restrictions; stadiums = stadium game-day TFR proximity (3 NM); recreational_flyer_sites = FAA-listed fixed flying sites. Example: ["laanc_grid","class_airspace","prohibited_areas","national_defense_tfr","special_use_airspace","part_time_nsufr","stadiums","recreational_flyer_sites"].
pointsYesLocations to check, in WGS84 decimal degrees. Each item is either an object like {"lat": 39.86, "lon": -104.67, "label": "Site A"} (label optional; latitude/longitude aliases accepted) or a "lat,lon" string like "39.86,-104.67". One result row is produced per point, and one Result event is charged per row. Example: [{"lat":39.86,"lon":-104.67,"label":"Denver Intl (KDEN) - Class B, LAANC ceiling 0 ft"},{"lat":40.8296,"lon":-73.9262,"label":"Yankee Stadium NYC - LAANC 300 ft + stadium TFR"},{"lat":42.1708,"lon":-72.6375,"label":"Westover ARB (KCEF) - Class D, LAANC NOT offered"},{"lat":38.9072,"lon":-101.05,"label":"Rural western Kansas - MOA overhead, floor 500 ft AGL"},{"lat":47.2,"lon":-108.6,"label":"Rura…(truncated).
maxPointsNoSafety cap on the number of points checked (and billed) in one run. Points beyond the cap are skipped with a warning. Applied by default if omitted: 500.
Behavior4/5

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

With no annotations, the description carries the burden. It discloses key behavioral traits: 'Verdict is gated - it never reads clear when a layer did not answer' and 'Reads live from the official government source.' It also notes billing per result row in the parameter description. This is valuable beyond a simple read/write hint.

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 concise and front-loaded, using just a few sentences to state purpose, key behavior, and source. It does list all layers, which is somewhat redundant with the schema but aids quick comprehension. No fluff.

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

Completeness3/5

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

Without an output schema, the description should clarify the response format. It mentions verdicts but does not state what the verdict object looks like (e.g., per-point results, layer-specific details). It covers batch limits and gating, but lacks explicit error handling or return structure, leaving some ambiguity.

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 thoroughly documents all three parameters. The description adds no additional parameter-specific semantics beyond mentioning batch lat/lon, which is already in the points parameter description. Baseline applies.

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's purpose: 'Batch lat/lon to FAA UAS airspace verdicts' with a specific list of layers. It distinguishes itself from sibling tools by focusing on drone airspace checks (LAANC, no-fly zones) rather than other government data domains.

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 usage (any drone airspace query), but does not explicitly discuss alternative tools or when not to use it. It mentions batch processing and live data, but lacks direct comparison to other tools or exclusions.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/malonestar/gov-data-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server